典型露天矿场景的图像呈现多类型复合噪声特征，信噪比较低且具有显著的空间异质性，现有深度学习模型大多直接迁移自然图像去噪架构，忽视了矿山遥感图像特有的噪声分布规律。针对该问题，提出了一种基于改进YOLOv5的矿山遥感图像去噪方法。针对传统YOLOv5在高噪声环境下性能不稳定的问题，引入了多尺度特征融合模块，以增强模型对不同尺寸噪声的识别能力，同时结合残差注意力机制，提升了模型对有用特征的提取能力，增强了去噪效果的鲁棒性。采用自适应噪声估计技术，根据图像不同区域的噪声特性动态调整去噪参数，实现了更为精准的噪声抑制。实验结果表明：改进YOLOv5在峰值信噪比（PSNR）和结构相似性指数（SSIM）上均显著优于其他经典去噪方法，相较原始YOLOv5，PSNR提高2.5 dB，SSIM提高了0.05；改进YOLOv5在所有噪声类型下均表现出色，尤其是在高斯噪声环境中，其PSNR和SSIM分别达32.5 dB和0.95，显著优于其他经典去噪方法。

The images of typical open-pit mining scenarios exhibit multi-type composite noise characteristics, with a low signal-to-noise ratio and significant spatial heterogeneity. Most existing deep learning models directly transfer denoising architectures from natural images, ignoring the unique noise distribution patterns of mining remote sensing images. To address the issue, a mine remote sensing image denoising method based on improved YOLOv5 was proposed. Considering the instability of traditional YOLOv5 in high-noise environments, a multi-scale feature fusion module was introduced to enhance the model's ability to recognize noise of different sizes. Additionally, a residual attention mechanism was incorporated to improve the extraction of useful features and enhance the robustness of the denoising effect. An adaptive noise estimation technique was employed to dynamically adjust denoising parameters based on the noise characteristics of different image regions, achieving more precise noise suppression. The experimental results showed that the improved YOLOv5 significantly outperformed other classical denoising methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Compared to the original YOLOv5, the PSNR value increased by 2.5 dB, and the SSIM improved by 0.05. The improved YOLOv5 performed well under all noise types, especially in Gaussian noise environments, where its PSNR and SSIM reached 32.5 dB and 0.95, respectively, significantly surpassing other classical denoising methods.