由于井下环境复杂，低光照以及安全帽的物体较小导致的检测效果不理想。针对复杂环境下的低质影像，提出一种改进YOLOv7的煤矿井下低质影像安全帽检测。首先，针对低光照条件下的图像特征易受噪声干扰的局限性，提出多尺度MELAN模块，通过构建多尺度注意力机制来捕捉更广泛的上下文信息，进而强化特征信息的提取，并有效抑制噪声的干扰；其次，在主干网络使用软池化和全维动态卷积构建OD-SMP模块，减少了特征映射中的信息弥散，保留了更多上下文信息，增强对小目标的检测能力；最后，对于煤矿井下不同光照和距离的复杂背景和环境，导致检测样本质量出现参差不齐的情况，使用Wise-IoU作为损失函数。实验结果表明：改进模型的平均精度为94.9%，比原来的YOLOv7模型高出13.5%，具有更好的检测效果。

Due to the complex underground environment, low lighting conditions, and the small size of hard hats, the detection results are not ideal. To address low-quality images in complex environments, an improved YOLOv7 for hard hat detection in low-quality images from underground coal mines was proposed. Firstly, addressing the limitation that image features were susceptible to noise interference under low-light conditions, a multi-scale MELAN module was introduced. By constructing a multi-scale attention mechanism, broader contextual information was captured, thereby enhancing feature extraction and effectively suppressing noise interference. Secondly, the OD-SMP module was constructed using soft pooling and full-dimensional dynamic convolution in the backbone network, which reduced information diffusion in feature mappings, retained more contextual information, and enhanced the detection capability for small targets. Finally, to address the varying quality of detection samples caused by the complex backgrounds and environments with different lighting and distances in underground coal mines, Wise-IoU was used as the loss function. Experimental results show that the average precision of the improved model is 94.9%, which is 13.5% higher than the original YOLOv7 model, demonstrating better detection performance.