Abstract: This paper proposes a foggy weather object detection model based on depth-wise separable convolution and attention mechanism,aiming to achieve fast and accurate detection of objects in foggy scenes.The model consists of a dehazing module and a detection module,which are jointly trained during the training process.To ensure the accuracy and real-time performance of the model in foggy scenes,the dehazing module adopts AODNet to perform dehazing processing on input images,reducing the interference of fog on the detected objects in the images.In the detection module,an improved version of the YOLOX_s model is used to output the confidence scores and position coordinates of the detected objects.To enhance the detection performance of the network,depth-wise separable convolution and attention mechanism are employed on the basis of YOLOX_s to improve the feature extraction capability and expand the receptive field of the feature maps.The proposed model can improve the detection accuracy of the model in foggy scenes without increasing the model parameters and computational complexity.Experimental results demonstrate that the proposed model performs excellently on the RTTS dataset and the synthesized foggy object detection dataset,effectively enhancing the detection accuracy in foggy weather scenarios.Compared to the baseline model,the average precision(mAP@50_95)is improved by 1.9% and 2.37% respectively.

Key words: Foggy scene, Object detection, Image dehazing, Depthwise separable convolution, Attention mechanism