An Improved YOLOv5-based Method for Dense Pedestrian Detection Under Complex Road Conditions
Aiming at the problem of low pedestrian detection accuracy in complex street scene environment,a new network YOLO-BEN is proposed based on the improvement of YOLOv5 network.The network uses a residual connection module Res2Net with hierarchical system to integrate with C3 module,enhancing fine-grained multi-scale feature representation.The paper adopts the Bi-level routing attention module to construct and prune a region level directed graph,and applies fine-grained atten-tion in the union of routing regions,enabling the network to have dynamic query aware sparsity and improving the feature extrac-tion ability of fuzzy images.We incorporate the EVC module to preserve local corner area information and compensate for the problem of information loss caused by occluded pedestrians.In this paper,NWD metric and original IoU metric are used to form a joint loss function,and a small target detection head is added to improve the effect of long-distance pedestrian detection.In the experiment,the method has achieved good results on self-made data sets and some WiderPerson data sets.Compared with the original network,the accuracy,recall and average accuracy of the improved network are increased by 2.8,4.3 and 3.9 percent-age points respectively.
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