基于增强残差卷积网络的改进YOLOv7-Tiny模型的全自动精密机织织物缺陷检测

IF 2.2 4区 工程技术 Q1 MATERIALS SCIENCE, TEXTILES
Jagadish Barman, Chung-Feng Jeffrey Kuo
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引用次数: 0

摘要

织物缺陷检测领域经历了一个以物体检测模型的发展为标志的变革过程。从传统方法到高级深度学习架构,这些模型已经解决了纺织行业的关键挑战。YOLOv7-tiny模型作为一种非凡的解决方案脱颖而出,在织物缺陷检测方面表现出前所未有的性能。其增强的体系结构解决了关键的行业问题,包括高分辨率图像、小缺陷尺寸和不平衡的数据集。因此,本文的目的是结合改进的YOLOv7模型来实时检测机织物缺陷。利用额外的卷积层、批归一化层和泄漏整流线性单元(CBL)层增强增强残差卷积网络(ERCN)增强了层次化特征提取,而双级连接技术增加了更丰富表征的复杂性。有效层聚合网络降级(ELAN-D)简化和优化了CBL层,强调了YOLOv7-tiny模型中针对目标目标的平衡方法。改进的yolov7微型模型在准确性和效率之间取得了微妙的平衡,这对纺织行业的实际应用至关重要。与其他模型相比,该模型的准确率在0.50阈值下为84%,在0.50:0.95阈值下为40%。该模型还具有98%的高精度,并以90帧/秒的检测速度运行,满足织物生产的实时需求。YOLOv7-tiny模型可以识别小到1毫米的缺陷,是自动化织物缺陷检测和优化纺织品质量管理流程的关键工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fully Automatic and Precisely Woven Fabric Defect Detection Using Improved YOLOv7-Tiny Model Utilizing Enhanced Residual Convolutional Network

The field of fabric defect detection has undergone a transformative journey marked by the evolution of object detection models. From traditional approaches to advanced deep learning architectures, these models have addressed crucial challenges in the textile industry. YOLOv7-tiny model stands out as a remarkable solution, demonstrating unprecedented performance in fabric defect detection. Its enhanced architecture addresses key industry issues, including high-resolution images, small defect sizes, and imbalanced datasets. Therefore, the aim of this paper is to incorporate the YOLOv7 model with improvements to detect woven fabric defects in real time. Augmenting the Enhanced Residual Convolutional Network (ERCN) with extra Convolutional, batch normalization and leaky rectified linear unit (CBL) layers enhances hierarchical feature extraction, while the two-concatenation technique adds complexity for richer representations. Reducing CBL layers in Efficient layer aggregation networks-downgrade (ELAN-D) streamlines and optimizes, emphasizing a balanced approach in the YOLOv7-tiny model for targeted objectives. The improved YOLOv7-tiny model excels in achieving a delicate balance between accuracy and efficiency, vital for practical applications in the textile sector. This model’s accuracy, with a mAP of 84% at a 0.50 threshold and 40% at 0.50:0.95 showed exceptional in comparisons to other models. The model also boasts a high accuracy of 98% and operates at a commendable detection speed of 90 fps, meeting real-time demands in fabric production. Recognizing defects as small as 1 mm, the YOLOv7-tiny model emerges as a pivotal tool in automating fabric defect detection and optimizing textile quality management processes.

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来源期刊
Fibers and Polymers
Fibers and Polymers 工程技术-材料科学:纺织
CiteScore
3.90
自引率
8.00%
发文量
267
审稿时长
3.9 months
期刊介绍: -Chemistry of Fiber Materials, Polymer Reactions and Synthesis- Physical Properties of Fibers, Polymer Blends and Composites- Fiber Spinning and Textile Processing, Polymer Physics, Morphology- Colorants and Dyeing, Polymer Analysis and Characterization- Chemical Aftertreatment of Textiles, Polymer Processing and Rheology- Textile and Apparel Science, Functional Polymers
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