摘要
为了提升铁路货检作业效率和质量,针对目前铁路货检工作主要依赖人工对高清图片进行查看,存在强度大、容易漏判等问题,在统计分析既有货检问题的种类、与车型的关系及所占比例基础上,采用深度学习技术,基于Faster-RCNN网络架构建立货检病害智能识别模型。经样本图片测试集测试表明,所提出的货检智能识别模型对于车顶异物、车门开启、防尘盖开启等7类问题图片的检出率接近80%,准确性超过90%,每张图片的检测速度约0.9 s,可满足现场实时、智能检测需求,从而为铁路货检向智能化发展提供有力的工具。
To improve operation efficiency and provide qualified service in cargo inspection, this paper addresses problems(such as heavy labor intensity and high rate of missing report) encountered in current cargo inspection work(which is mainly conducted by visual examination on High Definition pictures),and provides statistical analysis on categories of problems existing in cargo inspection, their relationship with vehicle types and the proportions they make up. Based on it, the in-depth learning technology is adopted and an intelligent recognition model for cargo defects inspection is constructed with the FasterRCNN network structure. Sample of tested pictures shows that the inspection rate of the above-mentioned intelligent recognition model is close to 80%, with the accuracy rate over 90%, focusing on 7 types of problems such as foreign body on the roof, irregular door opening and improper dust cover opening, and etc. According to the statistics, it takes only 0.9 second to inspect one picture. This speed could satisfy requirements on real-time inspection and intelligent testing on the spot, which provides solid support for railway cargo inspection towards intelligent development.
作者
柴雪松
张慧
辛向党
龚喆
李健超
于国丞
CHAI Xuesong;ZHANG Hui;XIN Xiangdang;GONG Zhe;LI Jianchao;YU Guocheng(Railway Engineering Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China;Freight Transport Division, China Railway Lanzhou Group Co., Ltd., Lanzhou 730000, Gansu, China;Wuhai Train Operation Depot, China Railway Hohhot Group Co., Ltd., Wuhai 016000, Inner Mongolia, China)
出处
《铁道货运》
2019年第6期22-27,共6页
Railway Freight Transport
基金
中国铁路总公司科技研究开发计划课题(N2018X008)
关键词
铁路货检作业
高清图像
海量数据
深度学习
智能货运
安全运输
Railway Cargo Inspection
High Definition Image
Massive Data
In-depth Learning
Intelligent Cargo Transport
Safe Transportation