Due to the frequent occurrence of pedestrian accidents caused by the blind spot of commercial vehicle drivers, the YOLOv5s-FE algorithm is proposed to detect pedestrians in the blind spot using YOLOv5s as a baseline model for this problem. The Swin Transformer Block structure is introduced in the Neck network to improve the feature extraction capability to capture the global information. To solve the problem of missed detection of small-target pedestrians, the feature fusion part in the network structure is added with an upsampling layer to get a larger size feature map and then fused with shallow features to preserve the localization information of small-target pedestrians. Through experimental verification, the precision, recall and accuracy are improved by 2.2%, 2.1%, and 3.4%, respectively, and the pedestrian target can be detected effectively.
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