基于计算机视觉技术的矿井电机车无人驾驶障碍物检测技术

doi:10.11872/j.issn.1005-2518.2021.01.089

Abstract

Abstract:

The mining of mineral resources is getting deeper and deeper，the working environment is bad，the employees are aging seriously，and the human cost is rising，which brings great challenges to the development of mining industry.Intelligent mine operation has become an inevitable trend.As a part of mine intellec-tualization，unmanned transportation system is very important for mine，which means the improvement of safety production efficiency can achieve zero injury and zero time loss.Considering the development demand of driverless electric vehicle and the traditional computer vision method，it is difficult to realize the real-time detection and location of obstacles.An intelligent obstacle detection method based on the combination of traditional computer vision technology and deep learning target detection algorithm YOLOv3 was proposed.First of all，the video data of obstacles in the driving area of electric locomotive（this paper calls it effective detection area） were collected，using an image annotation tool namely labelimg to make VOC data set，using YOLOv3 to train data set，according to the feedback results，adjust the parameters continuously to obtain the relative optimal parameters，and finally get the obstacle detection model.Then use the traditional computer vision technology to locate the track by edge，texture and other information，using the “3 neighborhood” search method to get the track line coordinate value of left and right track lines，and expand a certain distance to the outside of the track according to the distance information，extracting the effective detection area，thus reducing the computational complexity of the later obstacle detection，at the same time，gridding images，converting the coordinates of obstacles to the actual distance.Finally，using the obstacle detection model to detect the effective detection area and respond to the detection results.Experimental results show that the method can identify many objects with different characteristics in the driving area，such as electric locomotive，people，large falling rocks，etc.It can process 6 pictures per second，and the average accuracy of the actual data collected in the field reaches 93.2%，it has good performance in real-time and accuracy，and has a good effect in the underground mine scene.

Key words: underground mine, driverless electric locomotive, intelligent obstacle detection, computer vision, YOLOv3, effective detection area

CLC Number:

TD524

Jinghua WANG,Liguan WANG,Lin Bi. Obstacle Detection Technology of Mine Electric Locomotive Driverless Based on Computer Vision Technology[J].Gold Science and Technology, 2021, 29(1): 136-146.

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模型名称	平均单张测试时间/s	平均精确率MAP/%
YOLOv3	0.02	93.2
Mask R-CNN	0.11	94.5

障碍物类别	不同距离下的指令
障碍物类别	>10 m	5~10 m	<5 m
人	鸣笛	鸣笛减速	刹车
石块	减速	减速	刹车
电机车	鸣笛	鸣笛减速	刹车

障碍物类型	检测精确率/%
人	92.3
石块	90.0
电机车	97.3
平均精度均值	93.2
平均单帧检测时间	0.02 s

Obstacle Detection Technology of Mine Electric Locomotive Driverless Based on Computer Vision Technology

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