College of Information and Electronic, Shangqiu Institute of Technology

煤矿井下光照不足、粉尘遮挡，井下视频监控系统采集的人员目标在二维图像中表现为小目标或低可见度目标时，原始YOLOv3网络的Darknet53特征金字塔结构无法充分提取和保留目标的细节信息，导致定位结果不准确。针对上述问题，提出了一种低可见度环境下基于改进YOLOv3的井下人员定位方法。首先，结合β函数映射和帧间信息增强技术，提升低可见度环境下煤矿井下监控视频的清晰度。然后，采用更轻量级的Darknet−19替代YOLOv3中的Darknet53，并引入CIoU作为损失函数，利用改进YOLOv3识别增强后视频中的井下人员目标。最后，基于映射模型将识别到的目标从二维空间投影至三维空间，结合三维定位结果完成井下人员定位。选用某煤矿一段低可见度环境下井下监控视频进行实验，结果表明：① 经过基于改进YOLOv3的井下人员定位方法处理后的视频帧亮度、可见度和各项评价指标（平均灰度、平均对比度、信息熵与灰度谱带宽）较原始视频均有明显提升，整体光照条件得到显著改善，且处理后的视频帧对比度得到增强，目标和背景之间更易区分，证明了采用的图像增强技术的有效性。② 改进YOLOv3模型能准确识别视频帧中的井下工作人员，不存在漏识别问题。③ 采用已知位置的标定物或人工标注的的真实三维位置作为基准，计算投影结果与真实位置之间的偏差（偏差计算涵盖X，Y，Z方向上的距离偏差），其中X方向和Y方向上的偏差均小于0.2 m，Z方向上的偏差小于0.002 m，表明构建的映射模型的映射效果好且定位精度较高。

In coal mines, inadequate lighting and dust obstruction result in personnel targets captured by video monitoring systems appearing as small or low-visibility objects in two-dimensional images. The original YOLOv3 network's Darknet53 feature pyramid structure was insufficient for effectively extracting and preserving detailed information about these targets, leading to inaccurate localization. To address this issue, personnel localization method for low-visibility environments based on improved YOLOv3 was. First, the clarity of coal mine monitoring videos under low-visibility conditions was enhanced using a combination of β function mapping and inter-frame information enhancement techniques. Next, Darknet53 in YOLOv3 was replaced with the lighter Darknet-19, and CIoU was introduced as the loss function to optimize personnel target identification in the enhanced video. Finally, the identified targets were projected from two-dimensional space to three-dimensional space based on the mapping model, completing the personnel localization process. Experiments conducted on monitoring videos from a coal mine in low-visibility conditions revealed the following findings: ① After applying the improved YOLOv3, the brightness, visibility, and various evaluation metrics (average gray level, average contrast, information entropy, and gray spectral bandwidth) of the video frames demonstrated significant improvements compared to the original videos. There was a substantial enhancement in overall lighting conditions and contrast, facilitating better differentiation between targets and backgrounds, thereby validating the effectiveness of the image enhancement techniques employed. ② The improved YOLOv3 accurately identified personnel in the video frames, with no instances of missed detections. ③ Using calibrated objects or manually annotated real three-dimensional positions as benchmarks, the deviation between the projected results and the actual positions was calculated (covering distance deviations in the X, Y, and Z directions). The deviations in both the X and Y directions were below 0.2 m, while the deviation in the Z direction was below 0.002 m, indicating a high mapping effect and localization accuracy of the constructed mapping model.

河南省科技攻关项目(242102210111)。

路晓亚，李海芳. 低可见度环境下基于改进YOLOv3的井下人员定位方法[J]. 工矿自动化，2024，50（9）：130-137.

LU Xiaoya, LI Haifang. Personnel localization method for low-visibility environments based on improved YOLOv3[J]. Journal of Mine Automation，2024，50（9）：130-137.