Pedestrian Detection Model Algorithm Optimization Based on Deep Convolutional Neural Network

Abstract

Pedestrian detection is a classical problem in computer vision and has been a difficult problem to solve for a long time. Compared with face detection, it is very difficult to accurately detect pedestrians in various scenarios because of the complex posture of human body, larger deformation, and more serious problems such as attachment and occlusion. This paper focuses on the typical pedestrian detection model - YOLO model. Through experiments, the principle of pedestrian detection model algorithm and its model effect are studied to solve the difficulties in pedestrian detection. In YOLO, logistic regression is used to predict the object score of each boundary box, and multi-scale fusion is used to make prediction. By observing the mAP index, it is concluded that the YOLO algorithm has a good effect on single-label pedestrian detection, and the calculation efficiency is high.