Nowadays, with the continuous progress of hardware technology, artificial intelligence has been developed. In addition, many people have been applying deep learning methods of artificial intelligence to object recognition to gradually improve the effectiveness and speed of object recognition, and many projects related to deep learning have been started one after another. In this article, we will briefly explain how the model can recognize the image. It also introduces the YOLO algorithm, which is very popular in recent years.
What is the YOLO algorithm?
YOLO is an abbreviation of "You Only Live Once", which means "live in the moment" and was once a popular slang term. The author of the YOLO algorithm named the algorithm "You Only Look Once", which stands for object detection, concerning this meaning. Object detection is a relatively simple task in computer vision, which can find some specific objects in a picture, not only to identify the type of object but also to mark the location of the object.
The core idea of YOLO is to capture features from input photos of any size through convolutional layers, stacks, and residual nets, conceptually cutting them into SxS grids and having each grid predict three types of information, which are the displacement of the bounding box (center x, y coordinates, width of the bounding box, w, h), the confidence index of the bounding box (presence of the bounding box), and the probability of belonging to each category. Then, the IoU or its variant (GIoU, DIoU, CIouU) is compared with the correct answer, and the training process is adjusted to produce a more accurate bounding box.
Object Detection Applications
With the advancement of artificial intelligence technology, many applications have been gradually seen around our lives, such as checking whether people are wearing masks in the post-epidemic era; or object defect detection, such as road potholes, cracks in external walls and bridge piers; and other fields such as traffic flow analysis, medical image analysis, biological image analysis, industrial safety analysis, etc., all can use object detection technology to help us quickly analyze images.
Analyze the value of applications to provide higher-quality services
Since YOLO's technology is open, a variety of applications are rapidly emerging. Such as in the manufacturing industry can detect abnormal products, in agriculture can detect the location of rice, and then estimate the harvest, in the current epidemic can detect the human face, and thermal image temperature detection to determine whether the entry and exit personnel have a fever, in traffic can detect the flow of vehicles or pedestrians, and then control the seconds of traffic lights, so that the traffic is more efficient.
With the development of semiconductor and electronic technology, self-driving cars will become more and more popular in the future, among which object detection is one of the most important AI technologies for self-driving cars. The faster the car can distinguish more kinds of objects on the road, the more immediately the self-driving car can take appropriate responses, such as the need to slow down, brake sharply, make a small turn, or accelerate through, all have to be identified within one-hundredth of a second, or even a shorter time.
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