摘要
起重机运行状态实时检测是工业安全生产的重要保障之一。针对起重机部件提出基于多任务学习Mask R-CNN的分割与关键点网络结构,该结构由Mask R-CNN定位框与区域语义分割网络、DeepLabCut关键点检测网络构成;在吊钩桥式起重机中采集数据进行测试,利用贪婪线性搜索算法与贝叶斯优化算法,搜索得到此模型的最优超参数组合为:学习率0.005,批数2,学习率策略为余弦衰减。该模型测试误差为2.46个像素点,测试AP可达95%,像素点误差反映到实际误差在5 cm以内,满足实际检测需求,可拓展应用于无人化、自动化起重机运行状态监测。
Real time detection of crane operation status is one of the important guarantees for industrial safety production.A segmentation and key point network structure based on multi task learning mask R-CNN is proposed R-CNN crane key parts positioning frame and regional semantic segmentation network,crane key point detection network based on DeepLabCut;in a hook crane to collect data for testing,using greedy linear search algorithm and Bayesian optimization algorithm,the optimal super parameter combination of this model is:learning rate 0.005,batch size 2,learning rate strategy is cosine decay.The test error of the model is 2.46 pixels,and the test AP is up to 95%.The pixel error can be reflected within 5 cm of the actual error,which can meet the actual detection needs,and can be extended to the unmanned and automatic crane operation state monitoring.
作者
杨帆
梁敏健
杨宁祥
彭晓军
Yang Fan;Liang Minjian;Yang Ningxiang;Peng Xiaojun(School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China;Guangdong Institute of Special Equipment Inspection and Research Zhuhai Branch,Zhuhai 519002,China)
出处
《自动化与信息工程》
2021年第2期14-18,共5页
Automation & Information Engineering
基金
广东省特种设备检测研究院2019年度科技项目(2020JD09)。
关键词
桥式起重机
关键点识别
深度学习
卷积神经网络
多任务学习
语义分割
bridge crane
key point recognition
deep learning
convolutional neural networks
multi-task learning
semantic segmentation