Abstract
Clear monitoring images are crucial for the safe operation of belt conveyors in coal mines. However, in underground environments, low illumination and uneven brightness can significantly degrade image quality, thereby affecting the detection of foreign objects in coal flow and reducing the reliability of safety monitoring equipment. To address this issue, an improved CycleGAN-based low-illumination image enhancement algorithm is proposed, which employs a cycle generative adversarial network for unsupervised learning. In the generator network, first, a multi-scale convolutional feature enhancement module is designed to extract features from low-light images at multiple scales, obtaining richer feature information. Second, a lighting enhancement module is designed to achieve global brightness equalization and reduce contrast differences between different regions. Finally, a dynamic effective self-attention aggregation module is designed to suppress the generation of noise and artifacts. For the discriminator network, a global-local discriminator structure is designed to optimize overall illumination while adaptively enhancing shadow and highlight regions. Additionally, a self-feature preservation loss is introduced to constrain the semantic consistency before and after enhancement and avoid detail distortion. Experimental results show that our method achieves PSNR and SSIM values of 27.07 dB and 0.880, respectively, on the CUMT-BelT dataset. In the YOLOv11n fusion image enhancement algorithm foreign object detection task, mAP reaches 94.7%, providing a reference for real-time safety monitoring tasks in complex lighting scenarios in underground coal mines.