智能装配中基于YOLO v3的工业零件识别算法研究

传统装配系统中依靠人力进行重复性劳动,容易 由于人的操作具有疲劳性和人眼分辨 率有限等特点造成失误,为了避免浪费人工和时间,解决工厂环境中光线等不稳定因素,提 出了一种基于YOLO v3算法对形状多样的工业零件识别方法。在智能装配系统中根据视觉检 测结果判断零件种类,弥补了传统方法的不足,满足产品生产系统的节拍要求。改进后的YO LO v3网络模型使用k-means算法重新聚类预选框的参数,残差网络来减少网络的参数,结 合 多尺度方法、采用Mish激活函数提高精确度,使其更适合工业零件的小目标分类检测。该模 型以3D打印的工业零件制作数据集,实验表明与原有的YOLO v3算法对比,使用改进后的网 络模型具有良好的鲁棒 性,准确率提高了1.52%,时间提高了7.25 ms,实现精确实时地检测出智能装配系统中的零件种类。

Research on industrial parts recognition algorithm based on YOLO v3in intellige nt assembly

The traditional assembly system relies on manual labor for repetitive labor,which is easy to cause errors due to the fatigue and limited human eye re solution of human operations.In order to avoid wasting labor,time and solve un stable factors such as light in the factory environment,it is proposed a method for recognizing industrial parts with various shapes based on YOLO v3algorithm .In the intelligent assembly system,the types of parts are judged based on the visual inspection results,which makes up for the shortcomings of the tradition al methods and meets the beat requirements of the product production system.The improved YOLO v3network model uses the k-means algorithm to cluster the param e ters of the anchor box,and the residual network to reduce the network parameter s.Combined with the multi-scale method and the Mish activation function to imp r ove accuracy,it is more suitable for small industrial parts and target classifi cation detection.The model uses 3D printed industrial parts to make a data set. Experiments show that compared with the original YOLO v3algorithm,using the i mproved network model has good robustness,the accuracy rate is increased by 1.52%,and the time is increased by 7.25ms.Accurately detect the types of parts in the intelligent assembly system in real time.