Abstract: To address the low recognition accuracy of coal and rock images in underground mining environments with low illumination, high noise, and blurred boundaries, a coal and rock image recognition method integrating adversarial enhancement and Proxy Attention mechanisms is proposed. This method employs a dual U−shaped structure, forming a complete pipeline from image enhancement to semantic segmentation. The first U−shaped structure is the Coal−Enhance GAN quality enhancement module, which introduces the SRA−SA gated attention mechanism to achieve self-regularized brightness balance and spatial texture enhancement. It combines deformable convolutions and deformable RoI pooling modules to adaptively capture irregular edge structures, while a global-local discriminator with a relative discriminative strategy is used to improve the authenticity of the generated images, effectively clarifying the coal and rock images. The second U−shaped structure is the Proxy Swin−UNet semantic enhancement module, which focuses on extracting and representing high-order semantic features of the coal and rock regions from the enhanced images. The Proxy Attention module is embedded, promoting multi-scale feature interaction through proxy token bias and depthwise separable convolutions, thereby enabling precise segmentation of complex structures. Experiments on real underground coal and rock datasets show that: The enhancement module achieves an NIQE score of 3.121, improving by 6.6% and 24.4% compared to CycleGAN and RetinexNet, respectively; The segmentation module achieves an MIoU of 84.10% and an MPA of 81.91% under noise interference (noise ratio 0.05), significantly outperforming comparative models such as Swin−UNet; Ablation experiments validate the effectiveness of each module, with the complete model reducing the NIQE further to 2.874. Using the segmentation module alone under noise-free conditions, the MIoU is 85.34% and the MPA is 83.12%, which is significantly lower than the post-enhancement segmentation performance, demonstrating the crucial role of image enhancement in improving recognition accuracy. This method provides a visual perception solution for intelligent underground mining systems and holds significant engineering application value.