Optimization of Loss Function for Pedestrian Detection

Abstract

AbstractThe advanced intelligent driving assistance system has improved the current traffic congestion to a great extent and effectively reduced frequent traffic safety accidents. Pedestrian detection technology is the core of autonomous driving technology, and its accuracy, real-time and complexity will directly determine the safe operation of autonomous driving. In the case of heavy traffic, detecting a single pedestrian in a crowd is still a challenging problem. Considering the problem of mutual occlusion between pedestrians in dense crowds, an improved function algorithm based on YOLOv3 is proposed to optimize the loss function and increase the accuracy of detection by replacing the anchor frame. Experimental results show that this method can effectively reduce the missed detection rate, increase the average accuracy, and help improve the effectiveness of pedestrian occlusion detection, ensure accurate pedestrian detection in traffic congestion scenarios, and ensure driving safety.KeywordsComputer visionDeep learningPedestrian detectionLoss function