Introduction to object detection in Computer Vision [2025]
As humans, we often have trouble quickly identifying and counting all the objects around us, but computers, thanks to object detection technology, excellent in this field. This advanced AI capability allows machines to not only detect and enumerate objects in images or videos with remarkable precision, but also to classify them into several categories and to identify objects such as people, animals or vehicles, for example.
In addition, these systems can accurately pinpoint the exact location of an object within an image. This technological leap, which has evolved considerably over the past two decades, has opened up new horizons beyond AI research. It is essential in real applications, such as autonomous vehicles that interpret complex traffic scenarios, and in retail, to streamline payment processes (for example, it is a technique that is widely used in the latest automatic checkouts).
The latest object detection algorithms, constantly improving in terms of accuracy and speed, are transforming industries by improving computer vision tasks in automated surveillance, environmental monitoring, and even advanced health diagnostics, demonstrating the increasingly profound impact of AI in daily life.
With this article, we offer you a Introduction to object detection in Computer Vision, to provide you with an overview of the most advanced object detection methods and algorithms in AI.
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Introduction: the basics of object detection
Before we dive into the details of the “how,” let's first look at the “what.” What is object detection? In a concrete way? What is it for and how does it work? These are just a few of the questions we are trying to answer in this article.
Object detection: what is it?
Object detection is a cutting-edge technology in machine learning and deep learning that allows computers to accurately identify and locate objects within images or videos. It belongs to a branch of artificial intelligence called “Computer Vision”.
Computer object detection programs aim to replicate the complex processes of human vision through various training data and the orchestration of complex algorithms: machines perceive and understand the visual world with a level of precision and sophistication formerly reserved exclusively for human perception.
The field of “Computer Vision” is one of the most rapidly evolving fields. At the heart of its rapid progress is the important role of object detection. This article aims to provide you with a overview of the main essential concepts to the understanding of the mechanisms for detecting objects by a machine.
Let's make it simple: object detection involves the creation of surrounding boxes around previously identified objects. These surrounding boxes are used to precisely locate the exact positions of objects in a given scene or to track their movement within it.
Why is object detection important? It is already part of our daily lives...
The role of object detection in Computer Vision goes well beyond object identification; it is an essential mechanism for understanding complex visual contexts. This technology allows nuanced tasks such as distinguish instances of individual objects (instance segmentation), Understand scenes to generate descriptive texts (adding captions to images), and the continuous detection and tracking of objects in real time through video sequences.
In addition, its applications have spread to various areas, from improving public safety through the detection and tracking of pedestrians and vehicles, to the transformation of retail with checkouts that allow for automated payments, without the need to scan each item individually.
Advances in both machine and deep learning models and neural networks have taken object detection to new heights, allowing for real-time processing and high precision, important for dynamic environments such as autonomous driving or advanced surveillance systems. These developments highlight the transformative impact of object detection in technical developments but also in everyday life.
A simple explanation of the principle, a key concept in artificial intelligence
The idea is toTrain a computer program to recognize different types of objects, to detect and count objects, and then to automatically locate objects to their precise position to the pixel in new images.
To do this, the system is powered by thousands of annotated photos, in which each object of interest is identified by a”encompassing box“. For example, cats are delimited by blue squares, dogs by red squares, etc.
How does it work? The main steps
Based on multiple data, i.e. a variety of images and training data, The AI algorithm will progressively detect patterns, textures, and shapes common to test images used for each category and will learn to recognize them. It will then be able to automatically identify them in any new image.
Differences with image classification and semantic segmentation
Before we get into the technical aspects of object detection, let's look at what sets this technology apart from two other related image processing techniques: semantic image classification and segmentation.
How are image classification and object detection different concepts?
While the image classification is simply to assign a label to an image (for example, “beach”), without locating specific objects, object detection identifies each relevant object occurrence (umbrellas, people,...) in the input image and delineates its position in the input images.
Image classification involves running an entire image through a classifier, usually a deep neural network, to obtain a corresponding tag or tag. Classifiers analyze the complete image but do not provide information about the specific location of the labeled object within the image.
On the other hand, object detection is a more advanced technique which not only classifies objects but also delimits them by drawing an encompassing box around them.
What is the difference with semantic segmentation?
As for the semantic image segmentation, It is a technique that detects and separates several objects, with greater precision than a simple encompassing box. In semantic image segmentation, all pixels associated with a particular label are marked, but this method does not delineate the exact contours of each individual object.
On the other hand, object detection, rather than segmenting objects, Precisely delineate the positions of each object instance separated by enclosing them in surrounding boxes.
Finally, the instance segmentation combines the best of both worlds: this technique involves determining which pixels in an image belong to a specific object class. First, it identifies the instances of individual objects, and then it segmentates each instance into the detected bounding boxes, which are called regions of interest in this context.
Object Detection: A Brief Comparison with Other Computer Vision Techniques
Compared to facial recognition, which identifies a single type of object detection in real time, or text detection, which identifies written words, object detection is a much more complex technology. In fact, it must learn to identify and classify a multitude of objects, whose shapes change according to the angle of view.
Object detection models and algorithms
The art of object detection systems lies in the algorithms used. Without going into complex mathematical formulas (you can consult these resources if you are interested), we can distinguish 2 main families of approaches to object detectors: “one-shot” methods and two-step methods.
One-step or “one-shot” approaches
“One-shot” approaches, as their name suggests, attempt to carry out the entire analysis by Single pass. They apply a unique convolutional neural network directly to the image to simultaneously detect and classify objects.
YOLO example
The best known example of a one-shot algorithm is certainly YOLO (You Only Look Once). Thanks to a highly efficient neural architecture, it offers excellent results while being faster than its competitors. An ideal solution for real-time applications such as autonomous cars.
Two-step approaches
Object detection using R-CNN algorithms (convolutional neural networks on regions) is based on the following three processes:
1. Find areas in the image that could contain an object. These regions are called regional proposals.
2. Extract the characteristics of CNNs from the proposed regions and classify the objects using the extracted characteristics.
There are three variants of an R-CNN. Each variant attempts to optimize, accelerate, or improve the results of one or more of these processes.
R-CNN
The R-CNN detector first generates region proposals using an algorithm such as Edge Boxes. The proposed regions are cut from the image and resized. Then, CNN classifies these cut and resized regions. Finally, the boxes containing the proposed regions are refined by a support vector machine (SVM) that is trained using the characteristics of the CNN.
Fast R-CNN
As in the R-CNN detector, the Fast R-CNN detector also uses an algorithm like Edge Boxes to generate region proposals. Unlike the R-CNN detector, which cuts and resizes proposed regions, the Fast R-CNN detector processes the entire image. While an R-CNN detector must classify each region, Fast R-CNN groups together the characteristics of the CNN corresponding to each region proposal. Fast R-CNN is more efficient than R-CNN because in the Fast R-CNN detector, calculations for the regions that overlap are shared.
Faster R-CNN
The Faster R-CNN detector Adds a region proposal network (RPN) to generate region proposals directly into the network, instead of using an external algorithm like Edge Boxes. The RPN uses anchor boxes for object detection. Generating region proposals in the network is faster and better adapted to your data.
What approach should you choose?
There is no one-size-fits-all approach to object detection. Each method has its pros and cons. The choice of object detection methods depends on the target application and the constraints in terms of accuracy, speed, and resource consumption.
A few tips for choosing a detection model according to your use cases...
For example, for a drone that needs to scan pallets in a warehouse, a quick solution like YOLO will be more than enough. On the other hand, in a medical context where precision is crucial, a slower R-CNN model will generally be preferred, but with finer boundaries.
Object Detection at the Service of Daily Life
Although very advanced from a technological point of view, Object Detection already has numerous concrete applications for the general public. From unlocking smartphones through facial recognition to automatic social media moderation and industrial quality control, this technology simplifies and secures certain daily tasks that we don't always pay attention to.
Detecting People
Among the consumer applications of object detection algorithms that are already an integral part of our daily lives, The Detection and Recognition of Persons are undoubtedly the most widespread.
Worn by Rapid progress in Deep Learning and Machine Learning Algorithms In recent years, this complex task of locating humans in images and videos has improved dramatically, to the point of merging into many of our activities, often without our knowledge.
Everyday examples
Who has never unlocked their smartphone with a simple look, thanks to the Facial recognition ? These quick and easy identity verification techniques are made possible by the detection of faces. Another example: when you download a Profile photo on a social network, the detection models immediately spring into action to blur or block Inappropriate content. Finally, in our cities, smart cameras equipped with this technology automatically measures the respect of social distances or the wearing of masks to fight against epidemics.
Intelligent video surveillance thanks to AI
Object detection also automates the tasks of video surveillance, anomaly detection, anomaly detection, pedestrian detection, and artificial anomaly detection, whether in public spaces, retail outlets, or sensitive industrial sites.
Thanks to the live analysis of the captured images, the software can generate alerts when a suspicious package is abandoned or an individual crosses a prohibited barrier. It is an effective way to assist security guards by drawing their attention to relevant events.
Object detection for autonomous vehicles
Another area where object detection plays a key role is autonomous driving. To make their way through traffic, the vehicles of the future rely on a whole battery of video sensors that constantly scan the environment for pedestrians, cyclists, other cars or even animals, in order to adapt their trajectory in real time.
Models trained to detect hundreds of different types of objects allow us to analyze several flows simultaneously with remarkable precision, bringing greater safety to the roads of tomorrow.
Visual inspection in industry
Detecting defects in manufactured products is now much easier on production lines. Cameras equipped with artificial brains inspect every room looking for the smallest problem: missing paint, poorly positioned parts, scratches, etc. A considerable gain in productivity and traceability for manufacturers, all without human intervention!
Surgical video analysis to train Computer Vision models... for more accurate diagnoses
Surgical video images constitute a Complex and often noisy data stream captured by endoscopic cameras during critical medical procedures. Object detection technology plays a key role in identifying elusive abnormalities such as polyps or lesions, requiring immediate surgery. Moreover, one can imagine a world in which this technology performs an additional function by providing real-time updates to the medical team, allowing them to closely monitor the evolution of the surgical procedure.
Advantages and disadvantages of object detection models
Object detection is a powerful computer vision technique with its own strengths and limitations. Understanding when to use object detection and when to consider alternative methods is important for effectively solving problems in a variety of scenarios.
Here is an analysis of advantages and disadvantages of the various methods of detecting objects.
A few advantages...
Effective for medium sized objects
Object detection excels when it comes to objects that occupy a moderate portion of an image, typically ranging from 5% to 65% of the image area. It is competent to recognize objects of various sizes in this range.
Effective when object boundaries are clear
This technique is very effective in detecting objects with well-defined boundaries. Objects with distinct edges and shapes are particularly suitable for detection.
Recognition of Clusters
Object detection can identify Clusters of objects as a single entity. When objects are grouped closely together, it has the ability to deal with them collectively, which can be advantageous in a variety of applications.
High-speed location
Object detection processes can achieve real-time or near real time performance, often exceeding 15 frames per second (fps). This ability to locate quickly is invaluable in scenarios where speed is important.
Versatility for multi-object scenarios
Object detection is well suited for scenarios where multiple objects need to be identified simultaneously in an image or video sequence. This versatility is especially valuable in applications such as surveillance, where detecting various objects in a scene is critical for security and surveillance.
Numerous applications in the real world
Object detection has widespread applications in a variety of real world fields, including self-driving cars, medical imaging for tumor detection, and retail for inventory management. Its adaptability and precision contribute to its extensive usefulness.
... but also disadvantages:
Limitations for elongated objects and very irregular shapes
Object detection may not be optimal for elongated or very thin objects, such as a pencil. In such cases, the object may occupy a small fraction of the enclosing box, leading to a bias towards background pixels rather than the object itself.
Object detection can also be difficult with objects that have very irregular or complex shapes, such as irregularly shaped geological formations. Detection accuracy can be compromised when objects deviate significantly from standard shapes.
Inefficient for non-physical concepts
Objects that lack a tangible physical presence, such as descriptors like “sunny,” “bright,” or “tilted,” are best treated using image classification techniques. Object detection can struggle to effectively manage these abstract concepts.
Unsuitable when boundaries are ambiguous
When objects have fuzzy boundaries from different angles, semantic segmentation may be a more appropriate choice. For example, aerial images containing the sky, ground, or vegetation, which lack well-defined boundaries, are better segmented using this approach.
Occlusion management that can be challenging
Objects that are frequently obscured (partially hidden) can pose challenges for object detection. In such cases, if possible, instance segmentation is a preferred choice within two-stage detection networks, as it excels at understanding and segmenting hidden objects more accurately than basic bounding box detection.
Resource intensive
Implementing object detection models often requires substantial computing resources, including powerful GPUs or TPUs. This resource demand can be a limitation in resource-constrained environments or on edge devices with limited processing capabilities.
Complexity of the data annotation process
Creating high-quality training datasets for object detection models, which involve accurately marking object boundaries and categories, can be time consuming and labour-intensive. The quality of training data directly impacts model performance, making data annotation a critical consideration.
Limited to 2D space for better performance
Object detection works primarily in two-dimensional space and can encounter difficulties when it comes to identifying objects in three-dimensional environments, such as detecting objects in volumetric medical scans or in augmented reality applications where depth information is crucial.
The effectiveness of object detection depends on the specific characteristics of the objects and scenes you are dealing with. To make informed decisions, it is essential to assess whether object detection aligns with the nature of your problem or whether alternative techniques such as instance segmentation, image classification, or semantic segmentation could be better suited to achieve your goals. Understanding these nuances allows you to select the most appropriate approach for your unique computer vision needs.
In conclusion...
It's clear that object detection has already become an integral part of our daily lives, without our knowledge. Whether it is a question of moderating social networks or optimizing production lines, this artificial intelligence technology provides its share of discreet assistance.
However, amid remarkable achievements in object detection, we need to recognize the challenges that remain on the horizon. One of these challenges is the management of large volumes of training data and the multitude of angles and poses of objects. Although object detection has made significant progress in dealing with variations in object orientation, further advances are needed to strengthen its robustness in complex scenarios. Overcoming this challenge will require continuous innovation and refinement of object detection algorithms.
However, despite these challenges, the pace of progress in artificial intelligence remains relentless. With continuous research and development, it seems obvious that the object detection applications will continue to diversify and evolve. In the coming years, object detection techniques are expected to spread in areas such as health or environmental monitoring. In the field of health, they will contribute to the early detection of diseases through medical imaging, helping in the timely diagnosis and treatment of patients. In monitoring the environment, they will make it possible to monitor and mitigate the impacts of climate change.
In conclusion, although challenges remain, the trajectory of progress in artificial intelligence assures us that object detection is a promising technique which will benefit from being adopted by R&D teams, to build increasingly sophisticated industrial and consumer products.
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