摘要
为了解决曲轴瓦盖人工上料效率低下、易出错的难题,研究了基于注意力机制的曲轴瓦盖上料机器人视觉定位和检测方法,实现自动上料。针对图像特征不明显,在Faster R-CNN的特征提取网络引入注意力机制,将曲轴瓦盖图像不同位置的权重映射到特征通道,使深度学习模型能够更多地关注曲轴瓦盖的边缘和中心语义信息。为进一步提高定位精度,本文还改进了候选框生成方法和损失函数。实验结果表明,与传统机器学习方法及经典深度学习目标检测模型相比,检测速度达0.419 s,定位精度最优(IOU和GIOU分别为0.9413和0.9409)。该方法还具有良好的鲁棒性。现场测试表明,该方法引导上料机器人抓取和放置曲轴瓦盖组的成功率达95.14%,提升了发动机装配生产线的效率。
To solve the problem of low efficiency and error prone of manual feeding of the crankshaft bearing caps(CBCs),the visual location and detection method of CBC feeding robot based on attention mechanism is studied to realize automatic feeding.Aiming at the unapparent image features,the attention mechanism is introduced into the feature extraction network of Faster R-CNN to map the weights of the CBC image at different positions to the feature channel,so that the deep learning model can pay more attention to the edge and center semantic information of the CBC.To further improve the location accuracy,this paper also improves the candidate box generation method and loss function.Experiment results show that compared with those of traditional machine learning methods and classic deep learning target detection models,the detection speed of this method reaches 0.419 s,the location accuracies are the best(IOU and GIOU are 0.9413 and 0.9409,respectively).In addition,the proposed method possesses good robustness.On site test shows that the success rate for the method guide the feeding robot to grasp and place the CBCs reaches 95.14%,which improves the efficiency of the engine assembly line.
作者
朱江
杜瑞
李建奇
蔡慕尧
许海霞
Zhu Jiang;Du Rui;Li Jianqi;Cai Muyao;Xu Haixia(College of Automation and Electronics Information,Xiangtan University,Xiangtan 411105,China;School of Computing and Electrical Engineering,Hunan University of Arts and Science,Changde 415000,China;Hunan Province Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft,Changde 415000,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2021年第5期140-150,共11页
Chinese Journal of Scientific Instrument
基金
湖南省自然科学基金(2020JJ6063)
湖南省教育厅科学研究项目(18A360,19B393)
湖南省高等学校省特色学科(湘教通[2018]469)资助。
