Research on Infrared Image Target Detection Technology Based on YOLOv3 and Computer Vision

Abstract

Target detection technology is one of the basic topics in the field of computer vision, and it is also a hot spot, with a very broad application market. However, most of the current target detection technologies based on deep learning are aimed at visible light imaging technology, and there are very few researches on infrared imaging technology. Target detection based on deep learning implements the learning of more features by abstracting, extracting, processing and integrating the essential features of a large number of samples. Therefore, the use of deep learning target detection algorithms for infrared image pedestrian detection applications can make up for the shortcomings of traditional detection methods. YOLOv3 is currently a relatively balanced recognition algorithm. This article analyzes the principles and characteristics of the YOLOv3 series of algorithms to optimize multi-scale detection, which improves the detection accuracy and achieves a relative balance between detection accuracy and speed to a certain extent. This research hopes to provide efficient and feasible solutions and solutions for infrared target detection and recognition in the air through the application of deep learning technology.