Strip steel surface defect detecting method combined with a multi-layer attention mechanism network

Liu, Guohua; Ma, Qianwen

doi:10.1088/1361-6501/acb5b5

Cited by 17 publications

(9 citation statements)

References 31 publications

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“…This type of research primarily focuses on single-stage object detection networks, particularly the YOLO series algorithms. Liu & Ma (2023) improved the utilization of defect features by adjusting the receptive field at different scales and attention weight preferences through the addition of an expanded and weighted cross-stage feature pyramid network in the Neck. They maximized the extraction of useful information by enhancing the cross-stage partial connection with ResNet in the Backbone.…”

Section: Related Workmentioning

confidence: 99%

Unleashing the power of AI in detecting metal surface defects: an optimized YOLOv7-tiny model approach

Chen,

Zhou,

Gao

et al. 2024

PeerJ Computer Science

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The detection of surface defects on metal products during the production process is crucial for ensuring high-quality products. These defects also lead to significant losses in the high-tech industry. To address the issues of slow detection speed and low accuracy in traditional metal surface defect detection, an improved algorithm based on the YOLOv7-tiny model is proposed. Firstly, to enhance the feature extraction and fusion capabilities of the model, the depth aware convolution module (DAC) is introduced to replace all ELAN-T modules in the network. Secondly, the AWFP-Add module is added after the Concat module in the network’s Head section to strengthen the network’s ability to adaptively distinguish the importance of different features. Finally, in order to expedite model convergence and alleviate the problem of imbalanced positive and negative samples in the study, a new loss function called Focal-SIoU is used to replace the original model’s CIoU loss function. To validate the effectiveness of the proposed model, two industrial metal surface defect datasets, GC10-DET and NEU-DET, were employed in our experiments. Experimental results demonstrate that the improved algorithm achieved detection frame rates exceeding 100 fps on both datasets. Furthermore, the enhanced model achieved an mAP of 81% on the GC10-DET dataset and 80.1% on the NEU-DET dataset. Compared to the original YOLOv7-tiny algorithm, this represents an increase in mAP of nearly 11% and 9.2%, respectively. Moreover, when compared to other novel algorithms, our improved model demonstrated enhanced detection accuracy and significantly improved detection speed. These results collectively indicate that our proposed enhanced model effectively fulfills the industry’s demand for rapid and efficient detection and recognition of metal surface defects.

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Section: Related Workmentioning

confidence: 99%

Unleashing the power of AI in detecting metal surface defects: an optimized YOLOv7-tiny model approach

Chen,

Zhou,

Gao

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…With the continuous development of deep learning, applying it to defect detection can realize measurement of defects with high accuracy and fast speed, and adopting deep learning methods can meet the developing needs of industry for robustness and efficiency. Therefore, in different scenarios, effective detection of surface defects on different types of steel can be achieved [7][8][9][10][11]. In addition, defect detection methods based on deep learning have also been extended to other fields, such as lithium battery defects [12], turbine blades defects [13], and other scenarios.…”

Section: Introductionmentioning

confidence: 99%

A light weight multi-scale feature fusion steel surface defect detection model based on YOLOv8

Xie,

Sun,

2024

Meas. Sci. Technol.

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In industrial production, the steel surface may incur diﬀerent defects owing to the inﬂuence of external factors, thereby aﬀecting the performance of steel. With the increasing requirements for steel quality, achieving eﬃcient detection of steel surface defects is a diﬃcult problem that urgently needs to be solved. Traditional steel surface defect detection methods are limited by poor detection performance and slow detection speed. Therefore, a model named LMS-YOLO, based on YOLOv8, is proposed in this paper for achieving eﬃcient steel surface defect detection. Firstly, in backbone, the LMSMC module is designed to fuse with C2f to obtain C2f LMSMC, so as to extract the features of diﬀerent scales for fusion and achieve the light weight of the network. Meanwhile, the proposed attention mechanism EGAM was added to backbone to enhance cross dimensional information interaction and feature extraction capabilities, and to achieve a more eﬃcient attention mechanism. In neck, BiFPN is used to achieve better cross scale fusion eﬀect. Finally, the model uses three independent decouple heads for regression and classiﬁcation, and replaces CIoU with NWD as the regression loss to enhance the eﬀect of detecting small scale defects and improve the detection accuracy. The experimental results show that the proposed LMS-YOLO obtains 81.1mAP on NEU-DET and 80.5mAP on GC10-DET, which indicates that the model proposed in this paper has a better comprehensive performance compared with other methods in steel surface defect detection.

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“…Li et al [18] and Wang et al [19] improved upon the Yolov4 and Mask R-CNN base models, respectively, resulting in enhanced detection accuracy in both cases. Liu and Ma [20] proposed a defect detection method to solve the problem of feature map loss. In addition, many researchers have extended the application of object detection methodologies to other fields such as airborne object detection, 3D detection, and medical detection.…”

Section: Introductionmentioning

confidence: 99%

A steel surface defect detection model based on graph neural networks

Pang,

Tan

2024

Meas. Sci. Technol.

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Steel is an indispensable raw material in the construction industry. To avert catastrophic events such as building collapse, it is essential to detect minute defects on steel surfaces during production. However, this has been a persistent challenge due to the minuscule and dense nature of these defects. To this end, we propose an efficient defect detector called Vision Grapher with Hadamard (ViGh) , which employs a novel attention mecha-nism (HDmA) to establish local-to-local relationships within an image and integrates global relationships by graph convolution. With the HDmA module, we can not only fuse information under the same field of view, but also under different fields of view, which significantly enhances the richness of the acquired features. In addition, com-pared to convolutional neural networks, graph neural networks can utilize the contextual information in the image more effectively and resulting in better performance. We eval-uate our model on the NEU-DET and GC-10 benchmark datasets, which encompass six and ten types of defects on the surfaces of hot-rolled and cold-rolled steel, and our mod-el achieves a mean Average Precision (mAP) of 79.04% and 66.93% on the two datasets, respectively. The results demonstrate that our model significantly improves the accuracy of defect detection compared to existing methods.

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Strip steel surface defect detecting method combined with a multi-layer attention mechanism network

Cited by 17 publications

References 31 publications

Unleashing the power of AI in detecting metal surface defects: an optimized YOLOv7-tiny model approach

Unleashing the power of AI in detecting metal surface defects: an optimized YOLOv7-tiny model approach

A light weight multi-scale feature fusion steel surface defect detection model based on YOLOv8

A steel surface defect detection model based on graph neural networks

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