基于图像识别的输电线路轨道运输装备安全检测系统

Safety detection system of rail transportation equipment for transmission lines based on image recognition

为了提高林区山地输电线路轨道运输装备运行安全性,搭建基于图像识别的轨道运输装备安全检测系统.首先给出整个林区山地输电线路轨道运输装备电控系统;其次介绍了感知模块系统所用到的各类传感器;然后基于拆分注意力网络和自校准卷积的融合,采用Faster-RCNN算法得到更好的特征提取,并采用此改进的Faster-RCNN算法进行装备周围人员识别试验;最后基于QT开发输电线路轨道运输装备远程控制软件,并实现对装备的远程操控.结果表明:改进的Faster-RCNN算法在林区山地强光照和复杂环境下能够大幅度提高识别装备周围人员的准确性,图像识别平均精度均值mAP可达87.13%,高于常规Faster-RCNN的74.35%和级联Faster-RCNN的76.28%,充分证明改进的Faster-RCNN算法具备优良识别能力,保障林区山地输电线路轨道运输装备安全运行.

To improve the operational safety of rail transportation equipment for mountainous transmission lines in forest areas,the safety detection system of rail transportation equipment based on image recognition was established.The electronic control system of rail transportation equipment for mountainous transmission lines in the forest areas was provided,and the various sensors used in the perception module system were introduced.The Faster-RCNN algorithm based on the combination of split-attention networks and self-calibration convolutions was applied to obtain better feature extraction,and the improved Faster-RCNN algorithm was used for the surrounding personnel recognition experiments.The remote control software of rail transportation equipment for transmission lines was developed based on QT,and the remote control of the equipment was realized.The results show that the improved Faster-RCNN algorithm can significantly improve the accuracy of identifying personnel around equipment in strong lighting and complex environments of forest areas.The mean average precision of image recognition can reach 87.13%,which is 74.35%higher than conventional Faster-RCNN and 76.28%higher than cascaded Faster-RCNN.The results fully prove that the improved Faster-RCNN algorithm has excellent recognition ability and ensures the safe operation of railway transportation equipment for mountainous transmission lines in forest areas.