棉纺织技术2024，Vol.52Issue(12)：58-64,7.
基于小目标语义增强的机织物疵点检测方法
Defect detection method of woven fabric based on small target semantic enhancement
周星亚 1刘可心 1吴正香 1夏克尔·赛塔尔1
作者信息
摘要
Abstract
In woven fabric detection,there were some disadvantages,including worse detection effect on woven fabrics for small targets in the existing algorithms,difficulity in algorithm learning due to the influence of noise and uneven categories on the image.A learnable data augmentation network was proposed to be directly connected to YOLOv7 to achieve end-to-end training,so that the input image can be translated in training process to improve the input quality of the image.A kernel selection attention mechanism was designed and embedded in small target detection head of YOLOv7 to make the network pay more attention to the features of small targets.Finally,ELAN module was combined with a pixel-based Transformer to form ELAN-Transformer,which was introduced into the backbone network of YOLOv7.The limitation of ELAN module in dealing with small defect areas was overcomed.The semantic perception ability of the network to the defect area was significantly enhanced,the accuracy and robustness of the defect target were improved.The test results showed that the algorithm can better detect the small target woven fabrics defects on the dataset containing micro woven fabric defects,with a mAP@0.5 of 95.2％,an precision of 95.5％,and a recall rate of 2.8 percentage points higher compared with the original YOLOv7,which could meet the detection needs of textile enterprises for the detection of small defects in woven fabrics.关键词
目标检测/机织物疵点/YOLOv7/小目标语义/注意力机制Key words
target detection/woven fabric defect/YOLOv7/small target semantic/attention mechanism分类
轻工纺织引用本文复制引用
周星亚,刘可心,吴正香,夏克尔·赛塔尔..基于小目标语义增强的机织物疵点检测方法[J].棉纺织技术,2024,52(12):58-64,7.
