Aiming at the problems of large pedestrian flow and dense crowds in scenic spots and the low efficiency of existing target detection algorithms for detecting occluded targets and small targets and the large number of model parame-ters,a lightweight scenic pedestrian detection algorithm SSC-YOLOv8n based on YOLOv8n is proposed.Firstly,the spa-tial and channel reconstruction attention convolution SCC2fEMA module is proposed to significantly reduce the number of model parameters and thereby improve the detection speed of the model.Secondly,the refined slim-neck paradigm is adopted,and the GSConv and V0V-GSCSP modules are used to effectively reduce the number of model parameters while improving the learning ability of the model.In addition,a coordinate attention dynamic decoupling head is proposed to significantly enhance the model's perception and sensitivity to position information.Finally,in order to more accurately balance the samples,the Focal Loss function is introduced to further improve the detection accuracy and robustness of the model.Experimental results show that on the scenic pedestrian data set,compared with the original model,the improved model is reduced the number of model parameters by 52%,mAP@0.5 is increased by 2.1 percentage poins,and mAP@0.5:0.95 is increased by 1.4 percentage poins.It shows that on the VisDrone2019 data set,mAP@0.5 is increased by 3.9percentage points.The improved algorithm has stronger generalization performance and can be better suitable for pe-destrian detection tasks in scenic spots.
维普
万方数据