YOLOv5s‐GC‐Based Surface Defect Detection Method of Strip Steel

Li, Xi‐Xing; Yang, Rui; Zhou, Hong‐Di

doi:10.1002/srin.202300421

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…Wang et al 34 replaced the ELAN module in the YOLOv7 model with bottleneck transformer 3, improving model accuracy while making the network model more lightweight. Li et al 35 first developed ResNet-Mini to pre-classify the original dataset, reducing the computational burden. Then, they conducted image preprocessing to enhance defect features.…”

Section: Related Workmentioning

confidence: 99%

Lightweight strip steel defect detection algorithm based on improved YOLOv7

Lu,

Yu,

Liu

2024

Sci Rep

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The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting surface defects in steel strips, this paper introduces SS-YOLO (YOLOv7 for Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces the CBS module in the backbone network with a lightweight MobileNetv3 network, reducing the model size and accelerating the inference time. The D-SimSPPF module, which integrates depth separable convolution and a parameter-free attention mechanism, was specifically designed to replace the original SPPCSPC module within the YOLOv7 network, expanding the receptive field and reducing the number of network parameters. The parameter-free attention mechanism SimAM is incorporated into both the neck network and the prediction output section, enhancing the ability of the model to extract essential features of strip surface defects and improving detection accuracy. The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance between detection accuracy and speed while maintaining a lightweight profile.

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Section: Related Workmentioning

confidence: 99%

Lightweight strip steel defect detection algorithm based on improved YOLOv7

Lu,

Yu,

Liu

2024

Sci Rep

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YOLOv8n-GAM: an improved surface defect detection network for hot-rolled strip steel

Xu,

Xiao,

Zhao

et al. 2024

Eng. Res. Express

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Production defects caused by irresistible factors such as process design problems or differences in steel properties in strip production affect the economic benefits of the enterprise and threaten production safety. Traditional defect detection methods are difficult to achieve real-time and high-precision detection, so developing surface defect detection methods based on deep learning is of great significance for strip production. In order to effectively improve the accuracy of the deep learning model in detecting surface defects on hot-rolled strip, in this work we propose a real-time detection model for surface defects on strip steel based on the YOLOv8n model. Firstly, the newly convolutional layer Con5v is designed to replace the original convolutional layer in the neck, and an attention mechanism is added in front of each Con5v to improve the algorithm's ability to extract small target information. Secondly, an additional set of upsampled feature extraction units is added to the neck in order to enhance the spatial information of the feature map. Subsequently, a set of feature fusion units is incorporated and the convolutional layers in it are improved to provide better feature maps. Thirdly, the number of decoupling detection heads is increased to receive more high-quality features. The final experimental results show that YOLOv8n-GAM (YOLOv8 Nano Model with Global Attention Mechanism) achieves 81.4mAP and 82.0FPS on the NEU-DET dataset and 71.2mAP and 55.0FPS on the GC10-DET dataset, which are 5.7% and 6.9% higher than those of YOLOv8n, respectively. The model proposed in this paper achieves a comprehensive performance improvement in strip steel.

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Slim-YOLOv8: A fast and accurate algorithm for surface defect detection of steel strips

Zhao,

Liu,

Tao

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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Steel strip is an extremely important industrial material and is widely used in various industrial fields. During the production process, surface defects need to be detected quickly and accurately. This study proposes a model. This study proposes a new slim-YOLOv8 (lightweight YOLOv8) detection model. The model is based on YOLOv8 and adopts a lightweight design paradigm, which reduces the number of parameters of the model and enhances the detection real-time performance. At the same time, an online reparameterization method is introduced to enhance the feature extraction capability of the network without raising the inference cost, and to improve the model's detection accuracy for complex defects. Finally, an auxiliary training head that can provide richer gradient information is added to the model to help train the model while preventing model overfitting. The performance of slim-YOLOv8 in mean average precision and parameters was evaluated on the well-known steel strip surface defect detection dataset NEU-DET, reaching a mAP of 85.8% at IoU 0.50 and 50.3% in the IoU 0.50–0.95 range. This is an improvement of 8.3% and 3%, respectively, compared to the baseline model. Meanwhile, the number of parameters of the model was reduced from 3.0 M to 2.7 M, which is 7% lower than the baseline model. The experimental results show that slim-YOLOv8 uses a smaller number of parameters, but has higher accuracy and is able to detect various defects in the dataset well.

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YOLOv5s‐GC‐Based Surface Defect Detection Method of Strip Steel

Cited by 4 publications

References 38 publications

Lightweight strip steel defect detection algorithm based on improved YOLOv7

Lightweight strip steel defect detection algorithm based on improved YOLOv7

YOLOv8n-GAM: an improved surface defect detection network for hot-rolled strip steel

Slim-YOLOv8: A fast and accurate algorithm for surface defect detection of steel strips

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