A self correcting low-light object detection method based on pyramid edge enhancement
A low-light target detection method was proposed to overcome the problem of low overall brightness,contrast and limited edge features in low-light images,which lead to poor recognition and local-ization of target detection algorithms.Firstly,a low-light enhancement network was designed to utilize the advantages of image Gaussian pyramid,Retinex and dark-channel defogging in low-light image enhance-ment,and edge contour features were added to the dark-channel defogging algorithm to enhance the over-all luminance contrast while highlighting the edge features of the target.Secondly,to improve the accura-cy of feature extraction in the feature extraction section of RTDETR,a lightweight self correcting feature extraction network was designed to generate and correct the feature maps generated by the backbone fea-ture extraction network with smaller computational complexity,thereby improving the accuracy of object detection.The experimental results on the ExDark dataset shows that compared with the benchmark RT-DETR,the mAP improves by 2.34%,the recall improves by 2.09%,the parameter amount reduces by 4.95 M,the model size reduces by 13.31 MB,and the proposed method is able to effectively improve the performance of the target detection in the low-light scene.
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