基于深度学习的门机抓斗检测方法  

The detection method for grab of portal crane based on deep learning

作　　者：张文明[1,2] 刘向阳 李海滨[2] 李雅倩[2] Zhang Wenming;Liu Xiangyang;Li Haibin;Li Yaqian(School of Electrical Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China;Key Laboratory of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinhuangdao,Hebei 066004,China)

机构地区：[1]燕山大学电气工程学院,河北秦皇岛066004 [2]燕山大学工业计算机控制工程河北省重点实验室,河北秦皇岛066004

出　　处：《光电工程》2021年第1期14-23,共10页Opto-Electronic Engineering

基　　金：河北省自然科学基金资助项目(F2019203195)。

摘　　要：在港口门机抓斗装卸干散货的作业过程中,人眼观察无法精确判断抓斗所在位置,会带来工作效率低下及安全性等问题。为解决该问题首次提出了一种基于深度学习的门机抓斗检测方法。利用改进的深度卷积神经网络YOLOv3-tiny对抓斗数据集进行训练及测试,进而学习其内部特征表示。实验结果表明,基于深度学习的门机抓斗检测方法可实现门机抓斗检测速度每秒45帧,召回率高达95.78%,在很好满足检测实时性与准确性的同时,提高了工业现场作业的安全性及效率。In order to solve the problems of low work efficiency and safety caused by the inability of human eyes to accurately determine the position of the grab during the loading and unloading of dry bulk cargo by portal crane,a method of grab detection based on deep learning is proposed for the first time.The improved deep convolution neural network(YOLOv3-tiny)is used to train and test on the data set of grab,and then to learn its internal feature representation.The experimental results show that the detection method based on deep learning can achieve a detection speed of 45 frames per second and a recall rate of 95.78%.It can meet the real-time and accuracy of detection,and improve the safety and efficiency of work in the industrial field.

关 键 词：抓斗检测 深度学习 YOLOv3-tiny 空间金字塔池化 反转残差组 空洞卷积 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U653[自动化与计算机技术—计算机科学与技术]