Review of Deep Convolution Applied to Target Detection Algorithms

As one of the most fundamental and challenging tasks in computer vision, target detection aims to find out specific targets in images and to locate and classify them, and is now widely used in many fields such as industrial quality inspection, video surveillance and unmanned vehicles. In recent years, with the breakthroughs in computer hardware resources and depth convolution algorithms in image classification tasks, depth convolution-based target detection algorithms have gradually replaced the traditional target detection algorithms and achieved significant results in terms of accuracy and performance. This paper reviews the current research status of depth convolution-based target detection algorithms and possible future development directions. It introduces the authoritative datasets and evaluation metrics of target detection algorithms with the limitations of traditional target detection algorithms as a guide, and then reviews the research and development history of representative algorithms for depth convolution-based target detection in recent years with time and algorithm architecture as the main research lines. The network structures of one-stage, two-stage and other improved algorithms are compared and analyzed, and the characteristics, advantages and limitations of various target detection algorithms are summarized. Finally, the future trends are prospected in the light of current problems and challenges of target detection.