An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5

Lin, Guijuan; Liu, Keyu; Xia, Xuke; Yan, Ruopeng

doi:10.3390/s23010097

Cited by 29 publications

(15 citation statements)

References 39 publications

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“…However, this method has a slower detection speed and lower real-time performance. Lin et al 29 proposed a method to detect small objects using a sliding window and a multi-head self-attention mechanism, and introduced the Swin Transformer module into the YOLOv5 algorithm. This approach shortens the distance between different scales and improves the accuracy of small object detection.…”

Section: Related Workmentioning

confidence: 99%

AYOLOv7-tiny: Towards efficient defect detection in solid color circular weft fabric

Kang,

2023

Textile Research Journal

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This article presents a solid color circular weft fabric defect detection method based on AYOLOv7-tiny. The aim of the development of this network is to enable real-time defect detection in the production of large circular machine fabrics. The network has a good accuracy rate, fast detection speed, and a lightweight model. In the YOLOv7-tiny network, a space-to-depth layer followed by a non-strided convolution layer is introduced to enhance the feature extraction capability, improve image sharpness, address issues such as uneven grayscale and difficult detection of minor defects, and simplify the model complexity while reducing computation. Additionally, we combined the Squeeze and Excitation (SE) and Spatial Attention Module (SAM) based on Convolutional Block Attention Module (CBAM) to construct the Hybrid Attention Module (SC), which is integrated into the YOLOv7-tiny network. The SC module increases the weight of important features, enhances the feature extraction capability, improves image segmentation, and enhances the accuracy of the network's location information. Through extensive experiments with a dataset of large circular machine solid color circular weft fabric defect collected from an industrial site, the results show that AYOLOv7-tiny has a detection accuracy (mean average precision) of 98.7%, a detection speed of up to 333 frames per second, and a computational complexity of only 4.4 GFlops, which is better than the current mainstream surface defect detection models. The detection accuracy, detection speed, and model complexity of the AYOLOv7-tiny network all meet the real-time detection requirements of the industry and have been successfully used for real-time detection of large circular machine fabric defects.

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Section: Related Workmentioning

confidence: 99%

AYOLOv7-tiny: Towards efficient defect detection in solid color circular weft fabric

Kang,

2023

Textile Research Journal

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“…Detecting fabric defects during factory manufacturing is crucial for ensuring high-quality products. Lin et al [ 6 ] proposed an intelligent and efficient method for detecting fabric defects based on the YOLOv5 neural network. To overcome the challenges of detecting small and unbalanced defect patches, they modified the baseline YOLOv5 network using the Swin transformer backbone.…”

Section: Overview Of Contributionmentioning

confidence: 99%

Efficient Intelligence with Applications in Embedded Sensing

Zuo

Liu

2023

Sensors

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“…In recent years, with the rapid development of deep learning technology, deep learning has gained widespread attention in many fields [7][8][9][10] , as well as target detection. Target detection technology is mainly divided into single-stage algorithms and two-stage algorithms.…”

Section: Introductionmentioning

confidence: 99%

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

zeng,

Zhong

2024

Preprint

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Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale cracks and high costs in road damage detection, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (Pavement Disease). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the Large Separable Kernel Attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road diseases while reducing the computational load. Furthermore, we introduced Lightweight Shared Convolution Detection Head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement cracks.

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An Efficient and Intelligent Detection Method for Fabric Defects based on Improved YOLOv5

Cited by 29 publications

References 39 publications

AYOLOv7-tiny: Towards efficient defect detection in solid color circular weft fabric

AYOLOv7-tiny: Towards efficient defect detection in solid color circular weft fabric

Efficient Intelligence with Applications in Embedded Sensing

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

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