面向拥挤行人检测的改进YOLOv7算法OA北大核心CSTPCD

Aiming at the problem that the detection algorithm is prone to omission and false detection in crowded pedestrian detection scenarios,this study proposes an improved YOLOv7 crowded pedestrian detection algorithm.Introducing a BiFormer visual transformer and an improved RepConv and Channel Space Attention Module(CSAM)-based Efficient Layer Aggregation Network(RC-ELAN)module in the backbone network,the self-attention mechanism and the attention module enable the backbone network to focus more on the important features of the occluded pedestrians,effectively mitigating the adverse effects of the missing target features on the detection.The improved neck network based on the idea of a Bidirectional Feature Pyramid Network(BiFPN)is used,and the transposed convolution and improved Rep-ELAN-W module enable the model to efficiently utilize the small-target feature information in the middle and low-dimensional feature maps,effectively improving the small-target pedestrian detection performance of the model.The introduction of an Efficient Complete Intersection-over-Union(E-CIoU)loss function allows the model to further converge to a higher accuracy.Experimental results on the WiderPerson dataset containing a large number of small target-obscuring pedestrians demonstrate that the average accuracies of the improved YOLOv7 algorithm when the IoU thresholds are set to 0.5 and 0.5-0.95 are improved by 2.5 and 2.8,9.9 and 7.1,and 12.3 and 10.7 percentage points compared with the YOLOv7,YOLOv5,and YOLOX algorithms,respectively,which can be better applied to crowded pedestrian detection scenarios.