Aiming at the problems of limited adaptability, loss of details and unclear features faced by target detection in low-light environment, the edge-driven detection method ED_YOLO is proposed. Firstly, the HESM module is proposed to extract edge information through the Sobel operator, guide the interaction of multiple features, and improve the sensitivity of effective information. Secondly, the C2f_DRM module is designed to efficiently integrate local details and global context information. Then, the LFAM module is constructed. Based on shared convolution, the adaptive control method of features of different scales is optimized to effectively reduce the loss of detail information. Finally, the RepGFPN module is introduced to improve the multiscale feature extraction capability of the model by using reparameterization technology. Experimental results on the ExDark dataset show that the mAP50 of the proposed method reaches 72.17%, which is 2.87% higher than the original YOLOv8n, achieving better detection effect.