| 化春键,李秀琴,蒋毅,俞建峰,陈莹.基于YOLO-CFD的棉布微小微弱缺陷检测研究[J].电子测量与仪器学报,2025,39(4):152-162 |
| 基于YOLO-CFD的棉布微小微弱缺陷检测研究 |
| YOLO-CFD based research on detecting small and weak defects in cotton fabric |
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| DOI： |
| 中文关键词: 缺陷检测 棉布 YOLOv8s 微小微弱目标 多尺度 特征提取 |
| 英文关键词:defect detection cotton fabric YOLOv8s small and weak targets multi-scale feature extraction |
| 基金项目:国家自然科学基金（62173160）项目资助 |
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| Author | Institution |
| Hua Chunjian | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment & Technology, Wuxi 214122, Chin |
| Li Xiuqin | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment & Technology, Wuxi 214122, Chin |
| Jiang Yi | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment & Technology, Wuxi 214122, Chin |
| Yu Jianfeng | 1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; 2.Jiangsu Key Laboratory
of Advanced Food Manufacturing Equipment & Technology, Wuxi 214122, Chin |
| Chen Ying | School of Internet
of Things Engineering, Jiangnan University, Wuxi 214122, China |
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| 中文摘要: |
| 棉布表面缺陷直接决定了布匹质量与品质的高低，针对在棉布缺陷检测任务中，缺陷目标的尺度差异大和微小微弱缺陷所导致的误检和漏检等问题，提出一种基于YOLOv8s的棉布缺陷检测网络（YOLO-CFD）。首先，为了更好地适应缺陷的尺度变化，利用双层路由注意力机制思想，设计双层路由注意力快速空间金字塔池化模块（BRASPPF）；其次，为了提高微小微弱目标的特征提取和定位能力，使用SPDConv模块代替部分卷积，同时在颈部特征融合阶段增加一个小目标检测层；最后，为了降低交并比（IoU）对位置偏移的敏感度，设计NWIoU损失函数作为边界框回归损失函数。实验结果表明，YOLO-CFD网络模型在自制的棉布缺陷数据集上的平均精度均值（mAP）mAP@0.5可达87.2%，提高了16.5%，速度满足工业实时性检测需求。此外，在可视化实验中，YOLO-CFD网络模型显示出更全面的多尺度特征提取能力，可检测仅有12个像素点的棉粒、接头和污渍的小缺陷目标，并更加精准地关注到断经和破洞这类细长全局缺陷特征。算法相较于其他主流目标检测算法，具有更高缺陷检测性能，能够为棉布缺陷检测提供有效探索。 |
| 英文摘要: |
| The surface defects of cotton fabric directly determine the quality of the fabric. To address the problems of false detection and missed detection due to the significant scale variations and weak small defects in cotton fabric defect detection tasks, YOLO-CFD, a cotton fabric defect detection network based on YOLOv8s is proposed. Firstly, in order to better adapt to the scale changes of defects, a new module named BRASPPF is designed based on the Bi-Level Routing Attention mechanism; Secondly, in order to improve the feature extraction and localization ability of weak small targets, space to depth convolution blocks replaces partial convolution, and a small target detection layer is added in the neck feature fusion stage; Finally, in order to reduce the sensitivity of IoU to position shift, the NWIoU loss function is designed as the bounding box regression loss function. The experimental results show that the YOLO-CFD network model can achieve mAP@0.5 of 87.2% on the self-made cotton defect dataset, an increase of 16.5%, and the speed can meet the real-time detection requirements of industry. In addition, in the visualization experiment, the YOLO-CFD network model demonstrated a more comprehensive multi-scale feature extraction capability, which can detect weak small defect targets such as knots, splice and stains with only 12 pixels, and more accurately focus on slender global defect features such as broken end and holes. Compared to other mainstream object detection algorithms, the proposed algorithm has higher defect detection performance and can provide effective exploration for cotton fabric defect detection. |
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