基于上下文信息聚合YOLOv5的织物缺陷检测OACSTPCD
Fabric defect detection based on contextual information aggregation YOLOv5
为了解决目前主流检测方法无法有效捕获织物缺陷丰富的上下文信息,导致对背景相似的缺陷检测精度低及漏检率高的问题,提出一种能聚合上下文信息的改进YOLOv5模型(YOLO-CA模型).YOLO-CA模型通过在YOLOv5的骨干网络中加入双重交叉注意力模块(DCCA)捕获缺陷与背景丰富的上下文关系,使模型聚焦于缺陷区域,提高模型对缺陷的检出率,并结合跨阶段软池化特征金字塔模块(CSP-SoftSPPF)提取更详细的缺陷特征信息,进一步提高模型的检测精度…查看全部>>
In response to the problem that the current mainstream inspection methods cannot effectively capture the rich contextual information of fabric defects,resulting in lower detection accuracy and higher leakage rate of defects similar with background,an improved YOLOv5 model(YOLO-CA model)that could aggregate contextual information was proposed.Defects with context-rich contextual relationships were captured by the YOLO-CA model by incorporating a dual cross-at…查看全部>>
李静;郑文斌
成都信息工程大学,四川成都,610225成都信息工程大学,四川成都,610225
轻工业
深度学习织物缺陷检测YOLOv5模型注意力机制SoftPool池化
deep learningfabric defect detectionYOLOv5 modelattention mechanismSoftPool pooling
《棉纺织技术》 2024 (4)
30-39,10
四川省自然科学基金(2022NSFSC0571)四川省应用基础研究项目(2018JY0273,2019YJ0532)四川省重点实验室开放项目(SCVCVR2020.05VS)
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