Abstract: At present, the existing image enhancement techniques for underground coal mines suffer from insufficient stability and large fluctuations in the quality of generated images, which affect the accuracy of subsequent target detection. Meanwhile, target detection methods based on YOLOv8 also face certain limitations in low-light environments due to weakened image features and information loss. To address these problems, a low-light target detection algorithm for coal mines based on image enhancement and improved YOLOv8 was proposed. The Denoising Diffusion Probabilistic Model (DDPM) was used to denoise and enhance the original images, restoring illumination and detail information. Based on YOLOv8n, improvements were made by introducing a Low-Frequency Filter Enhancement Module (LEF) and a Feature Enhancement Module (FEM) to enhance feature extraction performance for low-light images. The original CIoU regression loss function in YOLOv8n was replaced with MPDIoU, yielding the YOLOv8-DLFM model. The YOLOv8-DLFM was then used for target detection to improve accuracy and robustness. Experimental results showed that: ① compared with mainstream image enhancement methods, DDPM achieved a peak signal-to-noise ratio of 28.379 dB, a structural similarity index of 0.886, and a perceptual similarity of 0.104, demonstrating superior image reconstruction quality and structural similarity. ② YOLOv8-DLFM exhibited excellent overall performance, with precision, recall, and mAP@0.5 reaching 0.878, 0.791, and 0.896, respectively, and a frame rate of 88.6 frames/s. Compared with the original YOLOv8n model, the precision, recall, and mAP@0.5 of YOLOv8-DLFM increased by 8.13%, 6.6%, and 8.74%, respectively. ③ Compared with mainstream target detection models, YOLOv8-DLFM demonstrated stronger robustness and higher detection accuracy under complex low-light environments. It also exhibited high robustness and adaptability under typical conditions such as target occlusion, lighting interference, sparse targets, and dense targets.