Abstract: In view of the difficulty,low detection accuracy and high missed detection rate of occluded pedestrian detection,a pedestrian detection network model based on multi-layer feature fusion is proposed based on structural optimization of YOLOv7 method,aiming at improving the accuracy of occluded pedestrian detection.The method is to use ELAN-C module in the feature extraction part of the backbone network to enhance the ability of extracting pedestrian feature information,so as to improve the accuracy of pedestrian detection.At the same time,the global attention mechanism is introduced into the multi-scale feature fusion part to form multi-layer feature fusion.Through inter-dimensional information interaction,especially the focus on location information,the representation of detection target features is enhanced and the accuracy of pedestrian detection is improved.In addition,in order to accelerate the convergence rate of the model,EIoU is used as a loss function to further improve the positioning accuracy of the detection frame.The model is trained and verified on the open data set CityPresons,and the log-average miss rate MR^－2 of the evaluation index is decreased Bare 0.55%,Partial 0.91%,Reasonable 1.78%,Heavy 1.68%,respectively,which effectively reduce the miss rate.

Key words: YOLOv7, Pedestrian detection, Feature extraction, Multi-scale fusion, Loss function optimization