Improved pedestrian detection based on YOLOv5s

Abstract

As one of the most fundamental areas in computer vision, pedestrian detection aims to locate each object instance with a bounding box to represent its position and boundary in an input image. Recently, pedestrian detection has been attracting extensive attention in academia and industry with its essential role in high-level vision research and actual downstream task. In this paper, we build a pedestrian detection algorithm based on YOLOv5s to locate the position of a pedestrian in the input in the image. To address the problem of complex background and the human body is not easily detected by occlusion, an improved Inception module is added to YOLOv5s to extract different scale feature information. In contrast, CBAM attention is added to the CSP structure, and the EIoU loss function is used to improve the accuracy and generalization of the network and achieve accurate bounding box regression. The model is trained on the pre-processed VOC 2012 dataset to build a pedestrian detection model. Compared with the original YOLOv5s model, pedestrian recognition's accuracy and average precision are improved by 2.5% and 3.1%, respectively.