面向站口行人检测的改进型Yolov5s算法OA北大核心CSTPCD

Aiming at the problem that existing pedestrian detection method is difficult to strike a balance between real-time performance and accuracy,an improved Yolov5s model is proposed for efficient pedestrian detection at station entrances.First,the lightweight main network Efficientnet_c is improved based on the improved EfficientNetV1,and the network structure and stacking times of basic units are optimized to enhance the feature extraction capability and speed of the model for small targets at the shallow layer.Secondly,by adjusting the width factor as 1/2 of the basic model,the channel number of feature layer of the model is changed,and the number of model parameters is reduced in the case of small precision loss.Thirdly,a small target detection layer is added to optimize the feature extraction ability of the model and improve the sensitivity and accuracy of the model to small targets.Finally,transfer learning is used to optimize the model,enhance the generalization ability of the model,reduce the learning cost,and further improve the accuracy of the model.The experimental results on the data set collected by the research group show that the accuracy of the proposed algorithm is 92.2％,and the number of model parameters is only 1.4 M.The average inference speed on Tesla P100 GPU is 7.7 ms,which realizes the improvement of model accuracy and inference speed.The results provide a feasible solution for pedestrian detection and traffic statistics of subway and railway station.