Due to reduced visibility and other reasons, the probability of accidents involving vehicles and pedestrians increases in foggy weather. In this paper, we present a real-time and efficient vehicle-pedestrian detection model for foggy images. In the backbone network, we added Mixed Aggregation Network (MANet) to replace C2f module to achieve stronger feature extraction. In the neck network, we introduced the Bidirectional Feature Pyramid Network (BIPFN) structure and used dysample for upsampling to achieve fast and efficient feature fusion in the model. In the loss function, we introduced Wise-...
