Object detection in foggy image based on Double-Head

algorithm has higher object detection accuracy.Image contrast in the foggy environment is low,and the object is fuzzy so that it is difficult to extract features in images. The existing object detection methods has a low accuracy for detecting objects in foggy images,and the objects is fuzzy and is difficult to extract features. To solve these problems,the feature extraction and prediction head are improved on the Double-Head framework. Firstly,multi-scale salient and effective features of objects in the image are carried out by adding channel attention to the feature maps extracted from the backbone network. Secondly,the prior matrix and fea-ture maps from the original image processing by dark channel prior method with image processing are fused to get more comprehensive feature information in foggy images. Finally,the separable convolution is introduced into the prediction head and the effective decoupled head is used to complete the classification and regression tasks. The proposed method has the mAP of 49. 37% on the RTTS dataset,and the AP of 66. 7% and 57. 7% on the S-KITTI and S-COCOval dataset. Compared with other mainstream algorithms, this