This article will provide an introduction to object detection and provide an overview of the state-of-the-art computer vision object detection algorithms. Object detection is a key field in artificial intelligence, allowing computer systems to “see” their environments by detecting objects in visual images or videos.
In particular, you will learn about:
- What object detection is and how it has evolved over the past 20 years
- Types of computer vision object detection methods
- We list examples, use cases, and object detection applications
- The most popular object detection algorithms today
- New object recognition algorithms
About: At viso.ai, we provide the end-to-end computer vision platform Viso Suite. The platform enables teams to build and deliver all their real-world computer vision applications in one place. Get the whitepaper and a demo for your company.
What is Object Detection?
Object detection is an important computer vision task used to detect instances of visual objects of certain classes (for example, humans, animals, cars, or buildings) in digital images such as photos or video frames. The goal of object detection is to develop computational models that provide the most fundamental information needed by computer vision applications: “What objects are where?”.
Person detection is a variant of object detection used to detect a primary class “person” in images or video frames. Detecting people in video streams is an important task in modern video surveillance systems. The recent deep learning algorithms provide robust person detection results. Most modern person detector techniques are trained on frontal and asymmetric views.
However, deep learning models such as YOLO that are trained for person detection on a frontal view data set still provide good results when applied for overhead view person counting (TPR of 95%, FPR up to 0.2%). See how companies use Viso Suite to build a custom people counting solution with deep learning for video analysis.
Why is Object Detection important?
Object detection is one of the fundamental problems of computer vision. It forms the basis of many other downstream computer vision tasks, for example, instance and image segmentation, image captioning, object tracking, and more. Specific object detection applications include pedestrian detection, animal detection, vehicle detection, people counting, face detection, text detection, pose detection, or number-plate recognition.
Object Detection and Deep Learning
In the last few years, the rapid advances in deep learning techniques have greatly accelerated the momentum of object detection technology. With deep learning networks and the computing power of GPUs, the performance of object detectors and trackers has greatly improved, achieving significant breakthroughs in object detection.
Machine learning (ML) is a branch of artificial intelligence (AI), and it essentially involves learning patterns from examples or sample data as the machine accesses the data and has the ability to learn from it (supervised learning on annotated images).
Deep Learning is a specialized form of machine learning which involves learning in different stages. To learn more about the technological background, check out our article: What’s the difference between Machine Learning and Deep Learning?
Latest technological advances in computer vision
Deep Learning object detection and tracking are the fundamental basis of a wide range of modern computer vision applications. For example, the detection of objects enables intelligent healthcare monitoring, autonomous driving, smart video surveillance, anomaly detection, robot vision, and much more. Each AI vision application usually requires a combination of different algorithms that form a flow (pipeline) of multiple processing steps.
AI imaging technology has greatly progressed in recent years. A wide range of cameras can be used, including commercial security and CCTV cameras. By using a cross-compatible AI software platform like Viso Suite, there is no need to buy AI cameras with built-in image recognition capabilities, because the digital video stream of essentially any video camera can be analyzed using object detection models. As a result, applications become more flexible as they no longer depend on custom sensors, expensive installation, and embedded hardware systems that must be replaced every 3-5 years.
Meanwhile, computing power has dramatically increased and is becoming much more efficient. In past years, computing platforms moved toward parallelization through multi-core processing, graphical processing units (GPU), and AI accelerators such as tensor processing units (TPU)
Such hardware allows applying computer vision for object detection and tracking in near real-time environments. Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection.
Those advances enabled a key architectural concept called Edge AI. This concept is also called Intelligent Edge or Distributed Edge. It moves heavy AI workloads from the Cloud closer to the data source. This results in distributed, scalable, and much more efficient systems that allow the use of computer vision in business and mission-critical systems.
Edge AI involves IoT or AIoT, on-device machine learning with Edge Devices, and requires complex infrastructure.At viso.ai, we enable organizations to build, deploy and scale their object detection applications while taking advantage of all those cutting-edge technologies. You can get the Whitepaper here.
Disadvantages and Advantages of Object Detection
Object detectors are incredibly flexible and can be trained for a wide range of tasks and custom, special-purpose applications. The automatic identification of objects, persons, and scenes can provide useful information to automate tasks (counting, inspection, verification, etc.) across the value chains of businesses.
However, the main disadvantage of object detectors is that they are computationally very expensive and require significant processing power. Especially, when object detection models are deployed at scale, the operating costs can quickly increase and challenge the economic viability of business use cases. Learn more in our related article What Does Computer Vision Cost?
How Object Detection works
Object detection can be performed using either traditional (1) image processing techniques or modern (2) deep learning networks.
- Image processing techniques generally don’t require historical data for training and are unsupervised in nature. OpenCV is a popular tool for image processing tasks.
- Pro’s: Hence, those tasks do not require annotated images, where humans labeled data manually (for supervised training).
- Con’s: These techniques are restricted to multiple factors, such as complex scenarios (without unicolor background), occlusion (partially hidden objects), illumination and shadows, and clutter effect.
- Deep Learning methods generally depend on supervised or unsupervised learning, with supervised methods being the standard in computer vision tasks. The performance is limited by the computation power of GPUs, which is rapidly increasing year by year.
- Pro’s: Deep learning object detection is significantly more robust to occlusion, complex scenes, and challenging illumination.
- Con’s: A huge amount of training data is required; the process of image annotation is labor-intensive and expensive. For example, labeling 500’000 images to train a custom DL object detection algorithm is considered a small dataset. However, many benchmark datasets (MS COCO, Caltech, KITTI, PASCAL VOC, V5) provide the availability of labeled data.
Today, deep learning object detection is widely accepted by researchers and adopted by computer vision companies to build commercial products.
Milestones in state-of-the-art Object Detection
The field of object detection is not as new as it may seem. In fact, object detection has evolved over the past 20 years. The progress of object detection is usually separated into two separate historical periods (before and after the introduction of Deep Learning):
Before 2014 – Traditional Object Detection period
- Viola-Jones Detector (2001), the pioneering work that started the development of traditional object detection methods
- HOG Detector (2006), a popular feature descriptor for object detection in computer vision and image processing
- DPM (2008) with the first introduction of bounding box regression
After 2014 – Deep Learning Detection period
Most important two-stage object detection algorithms
- RCNN and SPPNet (2014)
- Fast RCNN and Faster RCNN (2015)
- Mask R-CNN (2017)
- Pyramid Networks/FPN (2017)
- G-RCNN (2021)
Most important one-stageobject detection algorithms
There is also an algorithm named YOLOv8 that was published in 2022; however, it was not released by the creators of the original YOLO algorithms. To understand which algorithm is the best for a given use case, it is important to understand the main characteristics. First, we will look into the key differences between the relevant image recognition algorithms for object detection before discussing the individual algorithms.
One-stage vs. two-stage deep learning object detectors
As you can see in the list above, state-of-the-art object detection methods can be categorized into two main types: One-stage vs. two-stage object detectors.
In general, deep learning based object detectors extract features from the input image or video frame. An object detector solves two subsequent tasks:
- Task #1: Find an arbitrary number of objects (possibly even zero), and
- Task #2: Classify every single object and estimate its size with a bounding box.
To simplify the process, you can separate those tasks into two stages. Other methods combine both tasks into one step (single-stage detectors) to achieve higher performance at the cost of accuracy.
Two-stage detectors: In two-stage object detectors, the approximate object regions are proposed using deep features before these features are used for the image classification as well as bounding box regression for the object candidate.
- The two-stage architecture involves (1) object region proposal with conventional Computer Vision methods or deep networks, followed by (2) object classification based on features extracted from the proposed region with bounding-box regression.
- Two-stage methods achieve the highest detection accuracy but are typically slower. Because of the many inference steps per image, the performance (frames per second) is not as good as one-stage detectors.
- Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN. The latest evolution is the granulated RCNN (G-RCNN).
- Two-stage object detectors first find a region of interest and use this cropped region for classification. However, such multi-stage detectors are usually not end-to-end trainable because cropping is a non-differentiable operation.
One-stage detectors: One-stage detectors predict bounding boxes over the images without the region proposal step. This process consumes less time and can therefore be used in real-time applications.
- One-stage object detectors prioritize inference speed and are super fast but not as good at recognizing irregularly shaped objects or a group of small objects.
- The most popular one-stage detectors include the YOLO, SSD, and RetinaNet. The latest real-time detectors are YOLOv7 (2022), YOLOR (2021) and YOLOv4-Scaled (2020). View the benchmark comparisons below.
- The main advantages of object detection with single-stage algorithms include a generally faster detection speed and greater structural simplicity and efficiency compared to multi-stage detectors.
How to compare object detection algorithms
The most popular benchmark is the Microsoft COCO dataset. Different models are typically evaluated according to a Mean Average Precision (MAP) metric. In the following, we will compare the best real-time object detection algorithms.
It’s important to note that the algorithm selection depends on the use case and application; different algorithms excel at different tasks (e.g., Beta R-CNN shows the best results for Pedestrian Detection).
The best real-time object detection algorithm (Accuracy)
On the MS COCO dataset and based on the Average Precision (AP), the best real-time object detection algorithm is YOLOv7, followed by Vision Transformer (ViT) such as Swin and DualSwin, PP-YOLOE, YOLOR, YOLOv4, and EfficientDet.
The fastest real-time object detection algorithm (Inference time)
Also, on the MS COCO dataset, an important benchmark metric is inference time (ms/Frame, lower is better) or Frames per Second (FPS, higher is better). The rapid advances in computer vision technology are very visible when looking at inference time comparisons.
Based on current inference times (lower is better), YOLOv7 achieves 3.5ms per frame, compared to YOLOv4 12ms, or the popular YOLOv3 29ms. Note how the introduction of YOLO (one-stage detector) led to dramatically faster inference times compared to any previously established methods, such as the two-stage method Mask R-CNN (333ms).
On a technical level, it is pretty complex to compare different architectures and model versions in a meaningful way. And Edge AI is becoming an integral part of scalable AI solutions, newer algorithms come with a lighter-weight edge-optimized version (see YOLOv7-lite or TensorFlow Lite).
Object Detection Use Cases and Applications
The use cases involving object detection are very diverse; there are almost unlimited ways to make computers see like humans to automate manual tasks or create new, AI-powered products and services. It has been implemented in computer vision programs used for a range of applications, from sports production to productivity analytics. To find an extensive list of recent computer vision applications, I recommend you to check out our article about the 50+ Most Popular Computer Vision Applications in 2023.
Today, object recognition is the core of most vision-based AI software and programs. Object detection plays an important role in scene understanding, which is popular in security, construction, transportation, medical, and military use cases.
- Object detection in Retail. Strategically placed people counting systems throughout multiple retail stores are used to gather information about how customers spend their time and customer footfall. AI-based customer analysis to detect and track customers with cameras helps to gain an understanding of customer interaction and customer experience, optimize the store layout, and make operations more efficient. A popular use case is the detection of queues to reduce waiting time in retail stores.
- Autonomous Driving. Self-driving cars depend on object detection to recognize pedestrians, traffic signs, other vehicles, and more. For example, Tesla’s Autopilot AI heavily utilizes object detection to perceive environmental and surrounding threats, such as oncoming vehicles or obstacles.
- Animal detection in Agriculture. Object detection is used in agriculture for tasks such as counting, animal monitoring, and evaluation of the quality of agricultural products. Damaged produce can be detected while it is in processing using machine learning algorithms.
- People detection in Security. A wide range of security applications in video surveillance are based on object detection, for example, to detect people in restricted or dangerous areas, suicide prevention, or automating inspection tasks in remote locations with computer vision.
- Vehicle detection with AI in Transportation. Object recognition is used to detect and count vehicles for traffic analysis or to detect cars that stop in dangerous areas, for example, on crossroads or highways.
- Medical feature detection in Healthcare. Object detection has allowed for many breakthroughs in the medical community. Because medical diagnostics rely heavily on the study of images, scans, and photographs, object detection involving CT and MRI scans has become extremely useful for diagnosing diseases, for example, with ML algorithms for tumor detection.
Most Popular Object Detection Algorithms
Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once). The R-CNN’s are in the R-CNN family, while YOLO is part of the single-shot detector family. In the following, we will provide an overview and differences between the popular object detection algorithms.
YOLO – You Only Look Once
YOLO stands for “You Only Look Once”, it is a popular type of real-time object detection algorithms used in many commercial products by the largest tech companies that use computer vision. The original YOLO object detector was first released in 2016 and the new architecture was significantly faster than any other object detector.
Since then, multiple versions and variants of YOLO have been released, each providing a significant increase in performance and efficiency. Because various research teams released their own YOLO version, there were several controversies, for example, about YOLOv5. YOLOv4 is an improved version of YOLOv3. The main innovations are mosaic data enhancement, self-adversarial training, and cross mini-batch normalization.
YOLOv7 is the fastest and most accurate real-time object detection model for computer vision tasks. The official YOLOv7 paper was released in July 2022 by Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. Read our Guide about what’s new in YOLOv7.
SSD – Single-shot detector
SSD is a popular one-stage detector that can predict multiple classes. The method detects objects in images using a single deep neural network by discretizing the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location.
The object detector generates scores for the presence of each object category in each default box and adjusts the box to better fit the object shape. Also, the network combines predictions from multiple feature maps with different resolutions to handle objects of different sizes.
The SSD detector is easy to train and integrate into software systems that require an object detection component. In comparison to other single-stage methods, SSD has much better accuracy, even with smaller input image sizes.
R-CNN – Region-based Convolutional Neural Networks
Region-based convolutional neural networks or regions with CNN features (R-CNNs) are pioneering approaches that apply deep models to object detection. R-CNN models first select several proposed regions from an image (for example, anchor boxes are one type of selection method) and then label their categories and bounding boxes (e.g., offsets). These labels are created based on predefined classes given to the program. They then use a convolutional neural network to perform forward computation to extract features from each proposed area.
In R-CNN, the inputted image is first divided into nearly two thousand region sections, and then a convolutional neural network is applied for each region, respectively. The size of the regions is calculated, and the correct region is inserted into the neural network. It can be inferred that a detailed method like that can produce time constraints. Training time is significantly greater compared to YOLO because it classifies and creates bounding boxes individually, and a neural network is applied to one region at a time.
In 2015, Fast R-CNN was developed with the intention to cut down significantly on train time. While the original R-CNN independently computed the neural network features on each of as many as two thousand regions of interest, Fast R-CNN runs the neural network once on the whole image. This is very comparable to YOLO’s architecture, but YOLO remains a faster alternative to Fast R-CNN because of the simplicity of the code.
At the end of the network is a novel method known as Region of Interest (ROI) Pooling, which slices out each Region of Interest from the network’s output tensor, reshapes, and classifies it (Image Classification). This makes Fast R-CNN more accurate than the original R-CNN. However, because of this recognition technique, fewer data inputs are required to train Fast R-CNN and R-CNN detectors.
Mask R-CNN is an advancement of Fast R-CNN. The difference between the two is that Mask R-CNN added a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN; it can run at 5 fps. Read more about Mask R-CNN here.
SqueezeDet is the name of a deep neural network for computer vision that was released in 2016. SqueezeDet was specifically developed for autonomous driving, where it performs object detection using computer vision techniques. Like YOLO, it is a single-shot detector algorithm.
In SqueezeDet, convolutional layers are used only to extract feature maps but also as the output layer to compute bounding boxes and class probabilities. The detection pipeline of SqueezeDet models only contains single forward passes of neural networks, allowing them to be extremely fast.
MobileNet is a single-shot multi-box detection network used to run object detection tasks. This model is implemented using the Caffe framework. The model output is a typical vector containing the tracked object data, as previously described.
YOLOR is a novel object detector introduced in 2021. The algorithm applies implicit and explicit knowledge to the model training at the same time. Herefore, YOLOR can learn a general representation and complete multiple tasks through this general representation.
Implicit knowledge is integrated into explicit knowledge through kernel space alignment, prediction refinement, and multi-task learning. Through this method, YOLOR achieves greatly improved object detection performance results.
Compared to other object detection methods on the COCO dataset benchmark, the MAP of YOLOR is 3.8% higher than the PP-YOLOv2 at the same inference speed. Compared with the Scaled-YOLOv4, the inference speed has been increased by 88%, making it the fastest real-time object detector available today. Read more about the advantages of object detection using this algorithm in our dedicated article YOLOR – You Only Learn One Representation.
Object detection is one of the most fundamental and challenging problems in computer vision. As probably the most important computer vision technique, it has received great attention in recent years, especially with the success of deep learning methods that currently dominate the recent state-of-the-art detection methods.
Object detection methods are increasingly important for computer vision applications in any industry. If you enjoyed reading this article, I would suggest reading:
Object detection in videos can also be difficult because of the fast speed required of object detection algorithms to accurately classify and localise important objects in motion to meet real-time video processing. Another significant problem facing object detection is the limited amount of annotated data.What is the best object tracking algorithm for 2023? ›
DeepSORT is a good object tracking algorithm choice, and it is one of the most widely used object tracking frameworks.What is the latest in object detection? ›
The most popular one-stage detectors include the YOLO, SSD, and RetinaNet. The latest real-time detectors are YOLOv7 (2022), YOLOR (2021) and YOLOv4-Scaled (2020).Which Yolo version is best for object detection? ›
The YOLO v7 algorithm achieves the highest accuracy among all other real-time object detection models – while achieving 30 FPS or higher using a GPU V100.Why is object recognition so hard? ›
Introduction. Visual object recognition is an extremely difficult computational problem. The core problem is that each object in the world can cast an infinite number of different 2-D images onto the retina as the object's position, pose, lighting, and background vary relative to the viewer (e.g., ).Which object detection algorithm is better? ›
Region-based Convolutional Neural Networks (R-CNN)
Region-based convolutional neural networks significantly enhance object detection compared to HOG and SIFT.
The Pascal Visual Object Classes (VOC) dataset is a benchmark for object detection and classification in computer vision. It was created by the Visual Object Classes (VOC) project at the University of Oxford and has become a standard dataset for evaluating object detection algorithms.
A — Rotation — while rotating also means the bounding boxes get larger relative to the object, rotation appears to be the top augmentation. Object rotation agumentation (from the paper).What is the difference between object tracking and object detection? ›
This means object tracking is better suited for tracking a specific object's position and trajectory over time, while object detection can count similar objects identified by a common identifying label, such as person or vehicle.Is object detection an AI? ›
Object recognition is the area of artificial intelligence (AI) concerned with the abilities of robots and other AI implementations to recognize various things and entities.
Object detection forms a ground for other important AI vision techniques like image classification, image retrieval, or object co-segmentation that drives meaningful information out of real-life objects.Why YOLOv5 is better than YOLOv4? ›
Specifically, a weights file for YOLO v5 is 27 megabytes. Our weights file for YOLO v4 (with Darknet architecture) is 244 megabytes. YOLO v5 is nearly 90 percent smaller than YOLO v4.” So, it said to be that YOLO v5 is extremely fast and lightweight than YOLO v4, while the accuracy is on par with the YOLO v4 benchmark.Is Yolo faster than OpenCV? ›
YOLO: 380% faster object detection with OpenCV's NVIDIA GPU-enabled 'dnn' module. We are now ready to test our YOLO object detector. Make sure you have used the “Downloads” section of this tutorial to download the source code and pretrained models compatible with OpenCV's dnn module.Which algorithm is better than Yolo? ›
SSD is a deep-learning model for object detection and localization. Like YOLO, it uses a single forward pass for the recognition of objects from the whole image.What are the three stages of object recognition? ›
It is divided into three stages by the role of each stage: visual perception, descriptor generation, and object decision.What are the four main tasks of object recognition? ›
Agnosia is a rare disorder whereby a patient is unable to recognize and identify objects, persons, or sounds using one or more of their senses despite otherwise normally functioning senses. The deficit cannot be explained by memory, attention, language problems, or unfamiliarity with the stimuli.How do you train AI to detect objects? ›
- Step 1 — Preparing your dataset.
- Step 2 — Installing ImageAI and Dependencies.
- Step 3 — Initiate your detection model training.
- Step 4 — Evaluate your models.
- Step 5 — Detecting our custom Object in an image.
- — RESULT —
- Getting Started with Object Detection Using Deep Learning.
- Create Training Data for Object Detection. ...
- Create Object Detection Network.
- Train Detector and Evaluate Results.
- Detect Objects Using Deep Learning Detectors.
- Detect Objects Using Pretrained Object Detection Models.
- MathWorks GitHub.
- See Also.
Two-stage detectors divide the object detection task into two stages: extract RoIs (Region of interest), then classify and regress the RoIs. Examples of object detection architectures that are 2 stage oriented include R-CNN, Fast-RCNN, Faster-RCNN, Mask-RCNN and others. Let's take a look at the Mask R-CNN for instance.
You only look once (YOLO) is one of the most popular model architectures and algorithms for object detection. Usually, the first concept found on a Google search for algorithms on object detection is the YOLO architecture.Which algorithm is most effective? ›
The most efficient algorithm is one that takes the least amount of execution time and memory usage possible while still yielding a correct answer.Which CNN is used for object detection? ›
R-CNN stands for Region-based Convolutional Neural Network. The key concept behind the R-CNN series is region proposals. Region proposals are used to localize objects within an image. In the following blogs, I decided to write about different approaches and architectures used in Object Detection.How many images for object detection? ›
The data set must contain at least five images that have an object labeled for each defined object. For example, if you want to train the data set to recognize cars, you must add the "car" label to at least five images.
DOTA (Dataset for Object Detection in Aerial Images) is a large-scale dataset used for object detection in aerial images.What type of data is required for object detection? ›
To train an object detection model, you need to provide a dataset containing both the images and the corresponding location and label information.Is object detection better than image classification? ›
Image Classification helps us to classify what is contained in an image. Image Localization will specify the location of single object in an image whereas Object Detection specifies the location of multiple objects in the image. Finally, Image Segmentation will create a pixel wise mask of each object in the images.Can LiDAR be used for object detection? ›
LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The sensor is used in the perception system, especially object detection, to understand the driving environment.Which camera is used for object detection? ›
AI cameras use object detection algorithms to detect dangerous situations in real-time. This allows them to alert people immediately when something out of the ordinary is happening.What is an example of object detection? ›
A picture of a dog receives the label “dog”. A picture of two dogs, still receives the label “dog”. Object detection, on the other hand, draws a box around each dog and labels the box “dog”. The model predicts where each object is and what label should be applied.
Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results.Is object detection the same as face recognition? ›
The task of facial recognition involves recognising faces in images, while object detection entails determining the location of objects in images. To accomplish this goal, we have developed a model capable of detecting objects as well as recognizing faces.How do you tell if something was written by an AI? ›
- Method 1: Using Undetectable AI's Multi-Detection Tool.
- Method 2: Originality.ai Detector + Text Visualizer.
- Method 3: Content at Scale AI Detector (casual writing)
- Method 4: Copyleaks AI Detector.
- Method 5: Giant Language Model Test Room (casual writing)
Image recognition: Visual Search
Visual search uses real images (screenshots, web images, or photos) as an incentive to search the web. Current visual search technologies use artificial intelligence (AI) to understand the content and context of these images and return a list of related results.
AI algorithms can be trained to recognize patterns in data, such as handwriting, fingerprints or faces. They can be used to analyze written or spoken language, such as emails and text messages, as well as images and videos, to identify objects, people and events.How advanced will AI be in 2050? ›
But by 2050, AI will have 'profoundly' reshaped the world, Stakhov warns. He said: 'There is a dark AI future where those who control AI will gain huge power, while 99 percent of the population will be disenfranchised. The AI lords will control the world's data and turn the rest of us into their serfs.How intelligent will artificial intelligence become by 2030 any guesses? ›
According to futurist and engineer Ray Kurzweil, artificial intelligence will achieve human-level capability by 2030. This will be decided when AI is capable of passing a legitimate Turing test.What will AI look like in 2040? ›
By 2040, AI applications, in combination with other technologies, will benefit almost every aspect of life, including improved healthcare, safer and more efficient transportation, personalized education, improved software for everyday tasks, and increased agricultural crop yields.What are the disadvantages of YOLOv4? ›
Disadvantages. a. Comparatively low recall and more localization error compared to Faster R_CNN.Is CNN better than Yolo? ›
YOLO makes less than half the number of background errors as compared to Faster R-CNN. YOLO architecture enables end-to-end training and real-time speed while maintaining high average precision. Faster R-CNN offers end-to-end training as well but involves much more steps as compared to YOLO.
It struggles to detect smaller images within a group of images, such as a group of persons in a stadium. This is because each grid in YOLO architecture is designed for single object detection. Then, YOLO is unable to successfully detect new or unusual shapes.Which is the fastest algorithm for object detection? ›
YOLOv7 is the fastest and most accurate real-time object detection model for computer vision tasks.Why OpenCV is better than TensorFlow? ›
The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. OpenCV belongs to "Image Processing and Management" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".Which is better for object detection OpenCV or Tensorflow? ›
To summarize: Tensorflow is better than OpenCV for some use cases and OpenCV is better than Tensorflow in some other use cases. Tensorflow's points of strength are in the training side. OpenCV's points of strength are in the deployment side, if you're deploying your models as part of a C++ application/API/SDK.Is SSD faster than Yolo? ›
YOLO is blazing fast and uses little processing memory. While YOLOv1 was less accurate than SSD, YOLOv3 and YOLOv5 have surpassed SSD in accuracy and speed. In addition, YOLO can predict only 1 class per grid. If there are multiple objects in a grid, YOLO fails.Which neural network is best for object detection? ›
Most Popular Object Detection Algorithms. Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once). The R-CNN's are in the R-CNN family, while YOLO is part of the single-shot detector family.Which is faster SSD or Yolo? ›
4) Is Yolo faster than SSD? SSD is still considered to be one of the best object detection models. Still, given that it is somewhat more accurate, most of which occurs because of its ability to recognize things of different sizes, its speed is marginally slower when we compare it to YOLO.What is probability of object detection? ›
Probabilistic Object Detection is the task of detecting ob- jects in an image, while accurately quantifying the spatial and semantic uncertainties of the detections.Why is small object detection hard? ›
Small Object Detection is a computer vision task that involves detecting and localizing small objects in images or videos. This task is challenging due to the small size and low resolution of the objects, as well as other factors such as occlusion, background clutter, and variations in lighting conditions.Which language is best for object detection? ›
C++ is considered to be the fastest programming language, which is highly important for faster execution of heavy AI algorithms. A popular machine learning library TensorFlow is written in low-level C/C++ and is used for real-time image recognition systems.
- Viewpoint Variation. An object viewed from different angles may look completely different. ...
- Deformation. Many objects of interest are not rigid bodies and can be deformed in extreme ways. ...
- Occlusion. ...
- Illumination Conditions. ...
- Cluttered or Textured Background. ...
- Intra-Class Variation.
The major challenges in object detection are classifying objects and determining their position. Researchers are using a multi-task loss function to resolve these issues. This multi-task loss function helps in creating repercussions for errors in localisation and misclassifications both.
The experimental results show that BGD-YOLOX has a higher average accuracy rate in terms of small target detection, with mAP0. 5 up to 88.3% and mAP0. 95 up to 56.7%, which surpasses the most advanced object detection algorithms such as EfficientDet, CenterNet, and YOLOv4.Which CNN is best for small object detection? ›
Experiment results show that the augmented R-CNN algorithm improves the mean average precision by 29.8% over the original R-CNN algorithm on detecting small objects.How does AI object detection work? ›
Object detection is a computer vision technique that works to identify and locate objects within an image or video. Specifically, object detection draws bounding boxes around these detected objects, which allow us to locate where said objects are in (or how they move through) a given scene.Which model is better for object detection? ›
Region-based Convolutional Neural Networks (R-CNN)
Region-based convolutional neural networks significantly enhance object detection compared to HOG and SIFT.
Object recognition allows robots and AI programs to pick out and identify objects from inputs like video and still camera images. Methods used for object identification include 3D models, component identification, edge detection and analysis of appearances from different angles.What is the main purpose of object detection? ›
Object detection is a key technology behind advanced driver assistance systems (ADAS) that enable cars to detect driving lanes or perform pedestrian detection to improve road safety. Object detection is also useful in applications such as video surveillance or image retrieval systems.