Pedestrian Detection Algorithm Based on GCA-YOLOv5s
A pedestrian detection algorithm based on YOLOv5s improvement is proposed to address the issues of low accuracy and real-time performance in intelligent connected vehicle pedestrian target detection.First,the phantom module is used instead of the traditional convolution to reduce the model complexity while ensuring the model accuracy,thus improving the model real-time.Then,the coordinate attention module is introduced into the fea-ture extraction network to obtain important features and improve the pedestrian detection accuracy.Finally,the calcu-lation of the bounding box loss function is improved to address the drawbacks of the loss function calculation,and the power transform is introduced into the existing loss function to obtain higher accuracy of the bounding box regression.The experimental results show that the experimental mAP using this improved model on the Widerperson dataset rea-ches 70.8%,which is a 2.6%improvement compared to the original algorithm,and the detection speed reaches 61 FPS.The proposed algorithm improves both accuracy and detection speed compared to the mainstream algorithm.
万方数据
维普
