Abstract: Pedestrian detection is very important to ensure the safety of workers in underground coal mine. Due to the dark light and big dust of the underground environment, directly using YOLOv2to detect pedestrian is harmful for accuracy which is only 54.3%. To solve this problem, this paper proposes YOLOv2_PPM network which makes full use of context information of image based on YOLOv2 network and combines with pyramid pooling module of Pyramid Scene Parsing network（PSPnet）. Conducting experiment on the underground coal mine pedestrian dataset, YOLOv2_PPM network improves accuracy to 63.5% which increases 9.2% than YOLOv2 network, and achieves at 39 Frames Per Second（FPS）. When the size of input image is 480×480, detection accuracy increases to 71.6%, and achieves at 28 FPS which meets the requirement of real-time detection.

Key words: object detection, pedestrian detection, YOLOv2, Pyramid Scene Parsing Network（PSPnet）

摘要： 煤矿井下的行人检测对于保障井下作业人员的安全至关重要。煤矿井下光照暗、粉尘大，直接用YOLOv2检测井下行人，准确率低，仅达到54.3%。针对此问题，以YOLOv2网络为基础，结合了金字塔场景解析网络（PSPnet）中的金字塔池化模块，充分利用图片的上下文信息，提出了YOLOv2_PPM网络。在井下行人检测数据集上进行实验，YOLOv2_PPM网络的准确率提升到63.5%，较YOLOv2网络增加了9.2%，且速度达到了39?帧/s（FPS）。当输入图片的大小为480×480时，检测的准确率提升到71.6%，同时速度为28?帧/s，满足了实时检测的要求。

关键词: 目标检测, 行人检测, YOLOv2, 金字塔场景解析网络（PSPnet）