A dense pedestrian detection algorithm based on the improved YOLOv7 is proposed to ad-dress the issues of small target scale and occlusion in dense pedestrian detection scenarios.Firstly,the MobileNet attention module is introduced into the feature extraction network to reduce the model compu-tation and enhance feature extraction capabilities.Secondly,the addition of the BepC3 module in the feature fusion network enhances the ability of pedestrian multi-scale feature fusion.Finally,WD-Loss is used as the localization loss function to improve the localization accuracy of the model detection.Trai-ning and validation were conducted on the Wider-Person crowded pedestrian detection dataset,and the experimental results showed that the improved algorithm model AP50 achieved an accuracy of 0.784,leading the original YOLOv7 algorithm by 0.031.
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