Foggy Weather Object Detection Method Based on YOLOX_s
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 inter-ference 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 net-work,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 demon-strate that the proposed model performs excellently on the RTTS dataset and the synthesized foggy object detection dataset,ef-fectively 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.
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