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。
基于深度学习的门机抓斗检测方法
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通信作者： 2041203253@qq.com
出版日期：2021年1月15日
摘要
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基金项目：
河北省自然科学基金资助项目(F2019203195)
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引用本文：
张文明, 刘向阳, 李海滨, 等. 基于深度学习的门机抓斗检测方法[J]. 光电工程, 2021, 48(1): 200062.
