煤矿井下环境复杂，对部分作业现场人员行为进行检测时易出现漏检与误检问题。针对该问题，提出了一种煤矿井下暗光环境人员行为检测方法，包括暗光环境图像增强和行为检测2个部分。暗光环境图像增强基于自校准光照学习（SCI）进行改进，由图像增强网络和校准网络构成。人员行为检测通过引入Dynamic Head检测、跨尺度融合模块和Focal−EIoU损失函数来改进YOLOv8n模型。SCI+网络增强后的图像作为人员行为检测模型检测的对象，完成井下暗光环境人员行为的检测任务。实验结果表明：① 井下暗光环境人员行为检测方法的mAP@0.5为87.6%，较YOLOv8n提升了2.5%，较SSD，Faster RCNN，YOLOv5s，RT−DETR−L分别提升了15.7%，11.5%，0.9%，4.3%。② 井下暗光环境人员行为检测方法的参数量为3.6×10⁶个，计算量为11.6×10⁹，检测速度为95.24 帧/s。 ③ 在公开数据集EXDark上，井下暗光环境人员行为检测方法的mAP@0.5为74.7%，较YOLOv8n提升了1.5%，表明该方法具有较强的泛化能力。

The underground coal mine environment is complex, leading to missed and false detections when monitoring behaviors of mine workers under certain operational conditions. To address this issue, a method for detecting mine worker behaviors in low-light underground environments is proposed, which includes two parts: a low-light image enhancement and a behavior detection. The low-light image enhancement（SCI+） was improved based on self-calibrated illumination (SCI) learning, which consists ofan image enhancement network and a calibration network. The behavior detection improved the YOLOv8n model by incorporating the Dynamic Head detection, a cross-scale fusion module, and the Focal-EIoU loss function. Enhanced images from the SCI+ network were used as inputs to the behavior detection model to complete the tasks of mine worker behavior detection in low-light underground environments. Experimental results showed that: ① the method for mine worker behavior detection in low-light underground environments achieved an mAP@0.5 of 87.6%, representing an improvement of 2.5% over YOLOv8n, and improvements of 15.7%, 11.5%, 0.9%, and 4.3% compared to SSD, Faster RCNN, YOLOv5s, and RT-DETR-L, respectively. ② The method had a parameter count of 3.6×10⁶, a computational complexity of 11.6×10⁹, and a detection speed of 95.24 frames per second. ③ On the public EXDark dataset, the method achieved an mAP@0.5 of 74.7%, which was 1.5% higher than YOLOv8n, demonstrating strong generalization capability.