摘要
露天矿无人驾驶是智慧矿山建设的重要组成部分。露天矿无人驾驶的核心在于保障车辆安全行驶,然而现阶段矿区道路上存在的落石、水坑和车辙等障碍物严重影响了矿车的行驶安全。针对现有算法检测这类密集障碍物精度受限的问题,提出了一种基于RT-DETR的露天矿区路面障碍物检测算法。RTDETR算法通过在编码器阶段引入RepViT网络,提升了模型的特征提取效率,在解码器中通过通道压缩剪枝操作提升了模型的检测速度。此外,还提出融合注意力机制的RepAttC3模块,加强了模型的特征提取能力。通过采集不同矿区数据,构建了露天矿区路面障碍物数据集,并进行了障碍物检测试验。结果表明:本文所提出的RT-DETR算法的平均检测精度可达92.7%,综合检测精度可达96.6%,检测速度可达12.3 ms。相较于其他路面障碍物检测算法,本文算法对露天矿区多尺度与小目标障碍物具有良好的检测效果,能够为露天矿区车辆提供准确且高效的障碍物检测,保障车辆安全行驶。
Autonomous driving technology plays a crucial role in the development of smart mines,with its primary challenge being the safe navigation of vehicles within the intricate and dynamic environments of openpit mines.Mining roads are frequently characterized by a high density of diverse obstacles,including rockslides,water pits,and ruts,which present in various forms and are widely dispersed.These conditions pose substantial safety risks to the autonomous operation of mining vehicles.At present,although numerous road obstacle detection algorithms have been proposed,their detection accuracy is frequently constrained by the distinctive conditions present in open-pit mines,thereby hindering their ability to satisfy practical application requirements.This study presents a road obstacle detection algorithm for open-pit mines based on RT-DETR.The algorithm integrates the RepViT network within the encoder phase to augment the model’s feature extraction capabilities,thereby facilitating a more precise capture of the characteristic information of road obstacles.In the decoder section,the algorithm employs channel compression pruning techniques,which significantly decrease the model’s computational complexity and enhance detection speed.Furthermore,it incorporates the RepAttC3 module,augmented with an attention mechanism,thereby enhancing the model’s capability to detect multiscale and small target obstacles.To evaluate the algorithm’s efficacy,a dataset comprising road obstacle images from various mines,seasons,and scenarios was assembled,specifically focusing on open-pit mine road obstacles.The experimental findings indicate that the algorithm exhibits superior performance in identifying road obstacles within open-pit mines,achieving an average detection accuracy of 92.7%,a comprehensive detection accuracy of 96.6%,and a detection speed of 12.3 milliseconds.In comparison to existing road obstacle detection algorithms,the proposed algorithm demonstrates distinct advantages in detecting multi-scale and small target obstacles,thereby offering more precise and efficient obstacle detection for vehicles operating in open-pit mining environments.It offers robust technical support for the development of autonomous driving technology in open-pit mines,further advancing the progress of smart mine construction.
作者
陈良
CHEN Liang(Huizhou Jiaotou Lianghua Green Stone Quarry Co.,Ltd.,Huizhou 516323,Guangdong,China)
出处
《黄金科学技术》
北大核心
2025年第1期202-213,共12页
Gold Science and Technology
作者简介
陈良(1982-),男,江西抚州人,工程师,从事非煤矿山智能建设研究工作。cl10202@163.com。
