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Textile Defect Detection Algorithm Based on the Improved YOLOv8
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DOI：10.1109/ACCESS.2025.3528771.png)
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Abstract
En 中文
Automatic detection of textile defects is a crucial factor in improving textile quality. Fast and accurate detection of these defects is key to achieving automation in the textile industry. However, the detection of textile defects faces challenges such as small defect targets, low contrast between defects and the background, and significant variations in the aspect ratio of defects. To address these issues, this study proposes a new method for textile defect detection based on an improved version of You Only Look Once Version 8(YOLOv8) called DA-YOLOv8s. Deep & Cross Network(DCNv2) is introduced into the Backbone Network to replace the C2F module, enhancing the extraction of network features; an self-attention mechanism, Polarized Self-Attention(PSA), is adopted to increase feature fusion capability and reduce feature loss in both channel and spatial dimensions; finally, a Small Object Detection Head (SOHead) is added to improve the feature extraction ability for small targets. Experimental results show that the improved YOLOv8 algorithm achieves has achieved mAP@0.5 and mAP of 44.6% and 48.6% respectively, which is an improvement of 4.2% and 3.8% over the original algorithm, and also outperforms the Optimal YOLOv9s model and the latest YOLOv11s model in these two metrics. The speed of textile defect detection has reached 257.38 frames per second (FPS) and the floating-point operation speed is 36.6 GFLOPS, ensuring the accuracy and speed of textile defect detection, with practical engineering application value.
Keywords:
Feature extraction
Defect detection
Accuracy
Fabrics
Convolutional neural networks
Training
Textile industry
Gray-scale
Deep learning
Convolution
Interest point detection
textile industry
quality management
YOLOv8
textile defect detection
polarized self-attention
deep & cross network
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W