为降低机器人拾取强度，提高分拣效率，该研究提出了识别分拣残缺饼干的方法。基于机器视觉技术，采用三次拍照对比识别残缺饼干算法，解决了在拍照区域边缘位置因饼干只有部分被拍到会被识别为残缺品和部分饼干粘连导致误识别的问题；通过识别算法获取行进中的输送带上的饼干中心坐标位置，通过对比t时间间隔饼干中心坐标的差值是否等于输送带t时间运行的距离，以确认三次拍照获取的饼干图像哪些是同一饼干，并获取此饼干的图像特征数据；当获取的饼干图像特征数据与标准饼干的视觉特征参数不同时，识别为残缺品，输出该饼干中心坐标给并联机器人进行拾取。本文对程序的有效性进行了实验验证，并对影响识别率的因素进行了实验讨论。通过实验验证，程序运行可靠，对残缺饼干识别准确率保持在100%。另外镜头高度对识别准确率影响较小，但对可识别半径影响较大，呈线性关系；输送带运行速度增加至19 mm/s后，识别准确率直线下降；光照强度达到400 Lux后，识别准确率可保持100%。该研究建立的方法能够很好的对残缺饼干进行识别，为今后饼干在线检测分拣提供了技术支持。

In order to reduce the picking intensity of the robot and improve the sorting efficiency, a method for identifying and sorting incomplete/broken biscuits is proposed in this study. Based on machine vision technology, the three-photo-comparison recognition algorithm was used to solve the problem of misrecognition of incomplete/broken biscuits due to only a part of the biscuits being photographed at the edge of the photographing area. The center coordinate position of the biscuit on the moving conveyor belt was obtained via the recognition algorithm. Comparisons were made to determine whether the difference in the center coordinate of the biscuit at the time interval t was equal to the distance traveled by the conveyor belt during time t, to confirm which biscuit images obtained by three shots were from the same biscuit and obtain the image feature data of this cookie. When the obtained biscuit image feature data were different from the visual feature parameters of the standard biscuit, it was identified as a defective product, and the center coordinates of the biscuit were outputted to the parallel robot for picking. In this study, the effectiveness of the program was verified experimentally and the factors that would affect the recognition rate of conversion are experimentally examined. Through experimental verification, it was found that the program was reliable, and the accuracy of identifying incomplete/broken biscuits remained at 100%. In addition, the height of the lenss had a small effect on the recognition accuracy rate, but exerted a greater effect on the recognizable radius (showing a linear relationship). After the speed of the conveyor belt increases to 19 mm/s, the recognition accuracy decreased linearly. When the light intensity reached 400 Lux, the recognition accuracy could be maintained at 100%. The method established in this study can well identify the incomplete/broken biscuit and provide technical support for online detection and sorting of biscuits in the future.

广东省教育厅重点领域专项（6020210086K）；广东省普通高校重点领域专项（智能制造）（2020ZDZX2078)