煤矿井下巷道行驶车辆车前障碍物检测方法研究  

Research on detection method of vehicles road obstacle in underground roadway of coal mine

作　　者：闫明伟 YAN Mingwei(Qinglongsi Coal Mine Branch,Guoneng Yulin Energy Co.,Ltd.,Yulin 719408,China)

机构地区：[1]国能榆林能源有限责任公司、青龙寺煤矿分公司,陕西榆林719408

出　　处：《矿业安全与环保》2024年第1期168-174,共7页Mining Safety & Environmental Protection

基　　金：国家自然科学基金项目(52004207);陕西省自然科学基金项目(2022JM-314)。

摘　　要：为了解决巷道内运输车辆在行驶过程中对障碍物的检测易受到光照条件影响的问题,提出了一种基于图像增强的障碍物检测方法。该方法先利用VOC数据集格式制作井下车辆行驶过程中的障碍物数据集,然后利用MSR(Multi Scale Retinex)算法对井下采集到的低照度图像进行增强;通过改进CenterNet网络,设计了ResNetBN-18轻量级网络,再基于改进后的CenterNet目标检测算法训练数据集,最后实现对井下运输车辆车前障碍物的准确检测。实验结果表明,改进后的检测模型保持了原网络的高实时性,检测精度比原网络提高了10.1%,帧率提高了6.7%。In order to solve the problem that the obstacle detection of transport vehicles in roadway is easily affected by lighting conditions,an obstacle detection method based on image enhancement is proposed.Firstly,the VOC data set format was used to produce the obstacle data set in the process of underground vehicle driving,and then MSR(Multi Scale Retinex)algorithm was used to enhance the low-illumination image collected in the underground.By improving the CenterNet network,the ResNetBN-18 lightweight network was designed,and the data set was trained based on the improved CenterNet target detection algorithm.Finally,the accurate detection of vehicles road obstacle in underground roadway was realized.The experimental results show that the improved detection model maintains the high real-time performance of the original network,and the detection accuracy is improved by 10.1%,the frame rate increased by 6.7%.

关 键 词：井下运输车辆 低照度 障碍物检测 CenterNet网络 

分 类 号：TD634[矿业工程—矿山机电] TP391.41[自动化与计算机技术—计算机应用技术]