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

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）