Abstract
Pedestrian safety affects many fields such as driverless vehicles. In order to improve the precision of the pedestrian detection method and distinguish people-like objects from pedestrians, an improved YOLOv4 pedestrian detection method is proposed. Firstly, RepVGG Block is introduced into the feature extraction layer and feature fusion layer of YOLOv4 to improve the feature extraction ability of the network and reduce the loss of feature information. Then the SENet attention mechanism is introduced to make the algorithm focus more on the useful information. Finally, the SIoU loss function is introduced to the regression of the pedestrian target frame, which improves the convergence speed and reduces the blindness of the target frame. Experimental results show that, compared with the original YOLOv4 algorithm, this improved algorithm has higher detection precision, and it can also distinguish people from people-like objects on the published Pedestrian Detection Data set, with a detection precision of 83.7%.
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