How to use and train a YOLO model?
YOLO, developed in 2016, is a real-time object detection algorithm that is very popular in the fields of machine learning and computer vision. Unlike other methods that require multiple steps to detect objects in an image, YOLO accomplishes this task in one go, hence its acronym “You Only Look Once.”
In recent years, fast and precise algorithms have been implemented for recognizing objects in an image, such as YOLO.
This article aims to provide an overview of YOLO, how it works and how it has evolved since its creation. It will highlight the importance of YOLO in real-time object detection, emphasizing its ability to efficiently process images in a single pass.
YOLO can be used for real-time object detection in video surveillance systems, enabling the detection and tracking of objects or people. Below are some applications of the technology:
Surveillance and security
In the field of autonomous vehicles, YOLO can be used:
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To detect and classify objects on the road, such as pedestrians, vehicles, road signs and traffic lights.
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In driver assistance systems to detect obstacles, nearby vehicles and potential dangers on the road.
Autonomous automobile
Driving assistance
Autonomous vehicles and driver assistance systems are similar in their applications, but they have different goals. Assistance systems help drivers with functions like automatic emergency braking, while autonomous vehicles aim to drive entirely without human intervention.
YOLO can be applied for anomaly detection in medical images, facilitating early detection of diseases and medical conditions.
(Object detection in medical images - www.paperswithcode.com)
In e-commerce, YOLO can be used for object detection in product images, enabling more effective visual search and product recommendation.
Detection of faults on electronic cards
YOLO can be integrated into augmented reality applications to detect and track objects in the real world, providing interactive and immersive experiences for users.
Object detection for augmented reality
YOLO can be used to analyze videos to detect objects, brands and consumer behaviors, providing valuable data for marketing and advertising campaigns.
(Video analysis for marketing and advertising - www.tandemdirect.fr)
(Architecture and operation of Yolo for object detection)
It is an innovative design that enables fast and accurate object detection in a single pass through an image. This process begins by dividing the image into a grid of cells. Then, YOLO uses a convolutional neural network to extract features from the image at different scales, using convolutional, pooling and normalization layers. The detection layer is crucial, as it uses these extracted features to predict the bounding boxes of objects and the probabilities of their presence in each grid cell. This information is generated for each cell, breaking the image into a series of search areas for objects.
After that, YOLO uses a non-maximum removal technique to eliminate redundant bounding boxes and keep only the most reliable detections. Finally, the model output is a list of object detections, with each detection represented by a bounding box, a predicted class, and a confidence score associated with the detection. This comprehensive approach allows YOLO to achieve impressive performance in speed and accuracy in object detection, making it a popular choice for many application scenarios requiring fast and accurate image analysis.
Compared to other object detection models such as Faster R-CNN, SSD (Single Shot Multibox Detector) and RetinaNet, YOLO has both distinct advantages and disadvantages.
Benefits :
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High processing speed
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Ability to detect small objects
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Ease of implementation
Disadvantages:
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Lower accuracy for small objects
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Low recall for small or distant objects (details may be missing)
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Sensitivity to grid cell size
YOLO stands out for its speed and simplicity, while Faster R-CNN offers high precision but at a higher processing cost, SSD offers fast processing speed with competitive accuracy, and RetinaNet is designed for precise detection of small objects by overcoming class imbalance.
It is a family of real-time object detection models. There are different versions and sizes of these models, suitable for different performance and precision needs. For example, some versions are faster but less accurate, while others are more accurate but require more resources. By choosing the right version, one can find the right balance between speed and precision for specific applications.
To use a pre-trained YOLO model, you must first download the model from a reliable online source. Choose a tool or framework compatible with YOLO, such as TensorFlow or PyTorch, according to your preferences and skills. Then, install the necessary dependencies, like Python and related libraries, to run the model on your system.
Once the model is downloaded and the tools installed, you must follow the instructions to load it into your development environment. Prepare the images or videos you want to analyze by resizing them or converting them into a format suitable for the YOLO model.
Then use the model to detect objects in the prepared images or videos. Although this requires some coding, examples and tutorials are often available to guide you through this process.
If you want to see what objects are present in a photo, you can use a YOLO model to automatically detect them. It's useful for security, surveillance, or even just having fun with photos.
In summary, YOLO revolutionizes real-time object detection with its ability to detect everything in a single pass. Its advantages include high speed, accurate detection of small objects and ease of use, but it also has limitations such as lower accuracy for small objects. Using YOLO involves several steps, including downloading, uploading, and applying the template.
For the next article, we could explore the training and use of the FOMO (Faster Objects, More Objects) model, thus opening new perspectives on artificial intelligence applied to human and social interactions.
