Vehicle pedestrian detection algorithm at ferry entrance based on improved YOLOX

Abstract

This study introduces a number of enhancements to the YOLOX-S target detection network in an effort to address the issues of heavy traffic at the ferry, complex traffic environment, and sluggish detection speed. The conventional residual block in CSPDarknet, which has a significant number of parameters and high equipment requirements, is replaced by the MBConv module in the deep layer and by the Fuse-MBConv module in the shallow layer. This is completed for YOLOXS's backbone feature extraction network, CSPDarknet. The enhanced model's mAP value is 83.39%, 2.7% more than the baseline method. The experimental findings demonstrate that the enhanced method presented in this study is appropriate for the real-time detection of moving objects, such as cars and people, in the vicinity of the ferry entrance