A Real-Time Fabric Defect Detection Method Based on Improved YOLOv8
Source abstract
Abstract
Fabric defect detection is a crucial step in ensuring product quality within the textile industry. However, current detection methods face challenges in processing efficiency for high-resolution images, detail recovery during upsampling, and the adaptability of loss functions for low-quality samples, which limit detection accuracy and real-time performance. To overcome these limitations, this paper introduces an improved YOLOv8-based model that optimizes both aspects for fabric defect detection. First, we introduce an efficient RG-C2f module to improve processing speed for high-resolution images. Second, the DySample upsampling operator is adopted to enhance edge and detail preservation, improving detail recovery within defect regions. Finally, an adaptive inner-WIoU loss function is designed to dynamically adjust focus on low-quality samples, thereby strengthening the model’s generalization capability. Experimental results validated on the TILDA and Tianchi datasets show that, compared with YOLOv8, the proposed model achieves mAP improvements of 6.4% and 1.5%, respectively, demonstrating significant enhancements in detection accuracy and speed. This advancement provides strong support for fabric defect detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Currency Recognition and Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Technology, North University of China, Taiyuan 030051, China
Funding
the Natural Science Foundation of Shanxi Province
References (28)
Show source-provided references
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