关键词: 织物疵点, YOLOv5模型, 注意力机制, 深度学习

Abstract: China is the largest producer and consumer of textiles in the world, and the textile industry is a traditional industry in China. The industry affects people's life and employment issues, and plays an important role in the national economy. The widely used textile products are indispensable in people's lives and the quality of them not only affects people's lives, but the development of textile enterprises. Therefore, in the production process of textile products, the quality inspection is a very important link in the production chain.
For a long time, due to the limitations of the cloth making process and production equipment, the surface of the cloth is often stained, and there are broken figures and other defects. Traditional detection methods have many disadvantages. On the one hand, the detection efficiency of cloth inspection workers is relatively low, and a cloth inspector can find 200 defects per hour at most. Because some defects are small and difficult to be found, the defect detection rate is only 70%, and the concentration lasts half an hour at most. If this time range is exceeded, visual fatigue will occur. As workers rely on subjective experience when inspecting the quality of cloth, and long-term work will cause visual fatigue, missed inspections and wrong inspections will inevitably occur, leading to low defect detection efficiency of cloth. On the other hand, because the cloth inspection work requires a lot of eyesight and energy of the workers, it is difficult to recruit workers and the cost is high. If there is a missed inspection in the cloth inspection process and the defects are not picked out, the products produced will be classified as defective products even if the cloth has passed the follow-up cumbersome processing, which will cause certain economic losses to the enterprise.
With the development of artificial intelligence, all walks of life are moving towards intelligent production. Computer vision has replaced humans eyes and brains to observe the world through cameras and computers, automatically analyzing the collected videos or pictures. In the field of target detection, with the rise of deep learning, the performance of target detection has also been greatly improved.
To address such problems as unsatisfactory detection effects for small targets, we conducted research on the surface defects of cloth by combining with the deep learning network, proposing an algorithm model based on deep learning for defect detection and classification. This model can improve the detection accuracy and reduce the missed detection rate of small targets, which meets the needs of actual production for detection of cloth defects, and has certain practical significance on the intelligent development of the weaving industry.

Key words: fabric defects, YOLOv5 model, attention mechanism, deep learning