Abstract: In order to enhance the quality of fabric defect image detection, a parallel comprehensive learning particle swarm optimization algorithm is proposed. Firstly, the fabric defect in the image was obtained according to the light transmittance of the fabric. Secondly, through multi-scale utilization of gray value difference of fabric image, the saliency of the defect area was enhanced, and the defect was distinguished from the surrounding pixels, thereby weakening the influence of the background on the fabric defect. Thirdly, the local attraction factor was added based on comprehensive learning particles, and the multi-swarm and parallel strategies were adopted to improve the search capability. Finally, the algorithm flow was obtained. The simulation results show that the algorithm proposed in this paper can clearly detect the defect. The detection accuracy of damaged defect of 88.15%, the detection accuracy of missing defect of 90.46%, the detection accuracy of shifted defect of 93.87%, and the detection accuracy of broken warp defect of 86.54%. Its detection accuracy is higher than other algorithms, and consumes less time than other algorithms.

Key words: fabric, defect, comprehensive learning, parallel, particle swarm optimization algorithm, detection

摘要： 为了提高织物图像疵点检测的质量,提出了并行综合学习粒子群算法。首先,通过织物透光率获得织物图像的疵点;接着多尺度利用织物图像灰度值差异对疵点区域显著性增强,把疵点与周围像素进行区分,从而弱化背景对织物疵点的影响;然后综合学习粒子增设局部吸引因子,多群和并行策略提高搜索能力;最后得出算法流程。实验仿真显示本文算法对疵点检测清晰,破损疵点检测准确率为88.15%,缺失疵点检测准确率为90.46%,移位疵点检测准确率为93.87%,断经疵点检测准确率为86.54%,高于其它算法,同时检测消耗时间较少。

关键词: 织物, 疵点, 综合学习, 并行, 粒子群算法, 检测