计算机科学
人工智能
目标检测
特征(语言学)
计算机视觉
探测器
光学(聚焦)
模式识别(心理学)
语言学
电信
光学
物理
哲学
作者
Ying Wang,Zhidan Hao,Fang Zuo,Shuxiang Pan
出处
期刊:Journal of physics
[IOP Publishing]
日期:2021-09-01
卷期号:2010 (1): 012191-012191
被引量:12
标识
DOI:10.1088/1742-6596/2010/1/012191
摘要
Abstract Fabric defect detection is a key part of product quality assessment in the textile industry. It is important to achieve fast, accurate and efficient detection of fabric defects to improve productivity in the textile industry. For the problems of irregular shapes and many small objects, an improved YOLOv5 object detection algorithm for fabric defects is propose. In order to improve the detection accuracy of small objects, the ASFF(Adaptively Spatial Feature Fusion) feature fusion method is adopted to improve the PANet’s bad effect on multi-scale feature fusion. The transformer mechanisms can enhance fused features, allowing the network to focus on useful information. Experimental results show that the mean average precision of the improved YOLOv5 object detection algorithm in fabric defect map detection is 71.70%. The improved algorithm can quickly and accurately improve the accuracy of fabric defect detection and the accuracy of defect localization.
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