To address the challenges of low monitoring efficiency and poor accuracy in identifying illegal sand mining vessels in foggy conditions, this study proposes a deep learning-based identification method. First, an improved generative adversarial network is employed to dehaze collected images, producing clear water area images. The generator integrates the feature attention mechanism to extract complex texture features of vessels in foggy environments, while spectral normalization is added to the discriminator to prevent gradient vanishing during training. Cycle consistency loss is introduced to ensure structural consistency between generated and original images. The CBAM attention mechanism is integrated into YOLOv8 algorithm to improve feature extraction, enabling precise localization and identification of illegal sand mining vessels in dehazed images. The improved GAN achieves superior dehazing performance, with PSNR and SSIM values of 31.86 and 0.64, representing 3.6%~13.1% and 4.9%~56.1% improvements over Cycle GAN and GC-GAN, respectively. The YOLOv8 enhanced by CBAM achieves mAP50~95 of 89.6% and FPS of 36 on dehazed images, meeting the accuracy and speed requirements for practical law enforcement. The proposed method effectively enhances the informatization and intelligence of illegal sand mining supervision and enforcement in the Yangtze River Basin.