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 localization 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 enhancement， and edge contour features were added to the dark-channel defogging algorithm to enhance the overall luminance contrast while highlighting the edge features of the target. Secondly， to improve the accuracy 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 feature 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 RTDETR， 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.