基于机器视觉的编织袋表面缺陷检测系统设计与实现

目的 针对编织袋生产中表面缺陷检测效率和精度低等问题，应用机器视觉技术于编织袋表面缺陷检测，进而提高编织袋的生产效率。方法 基于机器视觉设计编织袋表面缺陷检测系统:首先为了降低背景灰度变化对缺陷检测的影响，研究一种同时具有噪声滤除与图像增强功能的预处理算法;其次选取二维最大熵值法对预处理后的编织袋图进行分割，并采用改进遗传算法对它进行优化以增强算法的收敛速度和效果;然后利用特征提取结合形态学处理的方法实现了编织袋表面缺陷的识别与分类;最后应用连通域进行分析，对分类出的缺陷进行统计与定位以获取缺陷的尺寸以及位置信息。结果 采集了200个编织袋缺陷样本，采用文中编织袋表面缺陷检测系统对编织袋样本进行缺陷识别，平均识别准确率为94.0%，处理一幅编织袋图像的时间约为600 ms。结论 该系统具有较高的识别效率和正确率，可实现编织袋表面缺陷的快速检测，满足工业生产的需求。

Design and Implementation of Woven Bag Surface Defect Detection SystemBased on Machine Vision

The work aims to apply machine vision technology to the surface defect detection of woven bags to solve the low efficiency and low accuracy of surface defect detection in the production of woven bags, and thus improve the production efficiency of woven bags. A surface defect detection system of woven bags was designed based on machine vision:Firstly, in order to reduce the effects of background gray changes on defect detection, a preprocessing algorithm with both noise filtering and image enhancement functions was studied. Secondly, the two-dimensional maximum entropy method was selected to segment the preprocessed woven bag map, and the improved genetic algorithm was used to optimize it to enhance the convergence speed and effect of the algorithm. Then, the recognition and classification of surface defects of woven bags were realized by feature extraction combined with morphological processing. Finally, the connected domain analysis was applied to count and locate the classified defects to obtain the size and location information of the defects. 200 defect samples of woven bag were collected, and the surface defect detection system of woven bags in this paper was used to identify defects of woven bag samples. The average recognition accuracy was 94.0%, and the processing time of an image of woven bag was about 600 ms. The system has high recognition efficiency and accuracy, and can realize rapid detection of surface defects of woven bags and meet the needs of industrial production.