关键词: 织物, 疵点, 综合学习, 并行, 粒子群算法, 检测

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