Dense pedestrian detection algorithm for intelligent driving
Aiming at the problems of poor generalization ability and low detection accuracy of dense pedestrian detection in in-telligent driving scenarios,an improved SF-YOLO algorithm is proposed.This method first introduces the parameter-free attention mechanism-SimAM attention mechanism to mine deeper relationships between feature channels and feature map spatial informa-tion,increase the receptive field of the neural network model,and enhance the model to obtain richer features in the feature extrac-tion stage.information;then draw lessons from the BiFPN idea in the Neck part to perform multi-scale feature fusion;secondly,add an information fusion module to combine key head information to improve the confidence of occluded pedestrians,suppress invalid features,reduce the difficulty of grid training,and improve the recognition of occluded pedestrians.ability.Finally,in terms of the selection method of the target detection frame in the post-processing stage,Soft Confluence was designed to replace the original al-gorithm,optimize the regression prediction of anchor,improve the situation of missed detection due to too close distance when pe-destrians are densely packed,and improve the convergence ability of the model.Experimental results show that the improved YOLO algorithm proposed in this article has a pedestrian detection accuracy of up to 92.1%in densely populated areas,which is 4.9 percentage point higher than the original model,and the average accuracy is increased by 6.1 percentage point.
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