结合深度学习与支持向量机的金属零件识别  

Metal part recognition based on deep learning and support vector machine

作　　者：郑健红 鲍官军[1] 张立彬[1] 荀一[1] 陈教料[1] Zheng Jianhong;Bao Guanjun;Zhang Libin;Xun Yi;Chen Jiaoliao(Key Laboratory of Special Purpose Equipment and Advaneed Manufacturing Technology,Ministry of Education,Zhejiang University of Technology,Hangzhou 310023,China)

机构地区：[1]浙江工业大学特种装备制造与先进加工技术教育部重点实验室

出　　处：《中国图象图形学报》2019年第12期2233-2242,共10页Journal of Image and Graphics

基　　金：NSFC-浙江省两化融合联合基金项目(U1509212);浙江省基金公益研究计划项目(LGG18E050023)

摘　　要：目的在视觉引导的工业机器人自动拾取研究中,关键技术难点之一是机器人抓取目标区域的识别问题。特别是金属零件,其表面的反光、随意摆放时相互遮挡等非结构化因素都给抓取区域的识别带来巨大的挑战。因此,本文提出一种结合深度学习和支持向量机的抓取区域识别方法。方法分别提取抓取区域的方向梯度直方图(HOG)和局部二进制模式(LBP)特征,利用主成分分析法(PCA)对融合后的特征进行降维,以此来训练支持向量机(SVM)分类器。通过训练Mask R-CNN(regions with convolutional neural network)神经网络完成抓取区域的初步分割。然后利用SVM对Mask R-CNN识别的抓取区域进行二次分类,完成对干扰区域的剔除。最后计算掩码完成实例分割,以此达到对抓取区域的精确识别。结果对于随机摆放的铜质金属零件,本文算法与单一的Mask R-CNN及多特征融合的SVM算法就识别准确率、错检率、漏检率3个指标进行了比较,结果表明本文算法在识别准确率上较Mask R-CNN和SVM算法分别提高了7%和25%,同时有效降低了错检率与漏检率。结论本文算法结合了Mask R-CNN与SVM两种方法,对于反光和遮挡情况具有一定的鲁棒性,同时有效地提升了目标识别的准确率。Objective Under the background of "machine substitution" robotic visual intelligence is crucial to the industrial upgrading of the manufacturing industry. Algorithm-guided industrial robots with a visual perception function are also receiving increasing attention in industrial production.One of the most critical difficulties in the automatic picking of industrial robots is the identification of the target area.This problem is particularly prominent in the picking process of metal parts. Unstructured factors, such as reflective surface and mutual occlusion during random placement, pose great challenges to the identification of the picking area.To solve these problems,this study proposes a picking region recognition method based on deep learning and support vector machine(SVM).These two models are combined to exploit their individual advantages and further improve their accuracy. Method The proposed approach is used to construct a new model that combines regions with a convolutional neural network feature(Mask R-CNN) and SVM.Our methods include feature extraction,multi-feature fusion,SVM classifier training,neural network training, the combination of SVM and deep neural network.First,the local binary pattern(LBP) and histogram of oriented gradient(HOG) features of the picking areaare extracted.The presence of interference areas poses a huge challenge to the identification of the picking area.The interference area is relative to the identification areaand is easily misidentified and obtained through long-term practice on the assembly line.The dimension of the feature matrix generated by directly merging these two features is too large.Thus, we mustutilize principal component analysis to reduce the dimensions of the feature matrix and train the SVM classifier through the trained feature matrix.The size of the matrix after the direct fusion of the two features is 7 000×2 692. Hence, we select a cumulative contribution rate of 94%, at which the recognition accuracy rate is up to 97.25%.The size of the feature matrix i

关 键 词：目标识别 多特征融合 支持向量机 深度学习 实例分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]