Dual-Branch Residual Network for Enhanced Steel Plate Fault Detection

Steel plate fault detection plays a crucial role in industrial manufacturing. However, the inherent complexity of steel plate fault data and the redundancy of certain features pose significant challenges for effective feature extraction. To address these challenges, we propose a dual-branch residual...

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Published in:Journal of advanced computational intelligence and intelligent informatics Vol. 29; no. 6; pp. 1311 - 1318
Main Authors: Chen, Hao, Lu, Jiaxin
Format: Journal Article
Language:English
Published: Tokyo Fuji Technology Press Co. Ltd 20.11.2025
Subjects:
Accuracy
Algorithms
Aluminum
Artificial neural networks
Datasets
Fault detection
Feature extraction
Informatics
Machine learning
Manufacturing
Neural networks
Principal components analysis
Product reliability
Steel plates
ISSN:1343-0130, 1883-8014
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Abstract Steel plate fault detection plays a crucial role in industrial manufacturing. However, the inherent complexity of steel plate fault data and the redundancy of certain features pose significant challenges for effective feature extraction. To address these challenges, we propose a dual-branch residual network model (DRNM), which utilizes a two-branch architecture. The first branch processes the original data through a convolutional neural network to capture local feature details, and the second branch leverages feature mapping to extract the spatial relationships within the data. To enhance feature extraction depth and model performance, residual networks are integrated into both branches, allowing for deeper network training and the capture of richer feature representations. The proposed dual feature extraction mechanism significantly improves the model’s representational power and fault-detection accuracy. Experimental results on a public dataset demonstrate that DRNM achieves state-of-the-art performance, with average recall and F1 score of 90.11% and 90.79%, respectively, substantially outperforming existing methods.
AbstractList Steel plate fault detection plays a crucial role in industrial manufacturing. However, the inherent complexity of steel plate fault data and the redundancy of certain features pose significant challenges for effective feature extraction. To address these challenges, we propose a dual-branch residual network model (DRNM), which utilizes a two-branch architecture. The first branch processes the original data through a convolutional neural network to capture local feature details, and the second branch leverages feature mapping to extract the spatial relationships within the data. To enhance feature extraction depth and model performance, residual networks are integrated into both branches, allowing for deeper network training and the capture of richer feature representations. The proposed dual feature extraction mechanism significantly improves the model’s representational power and fault-detection accuracy. Experimental results on a public dataset demonstrate that DRNM achieves state-of-the-art performance, with average recall and F1 score of 90.11% and 90.79%, respectively, substantially outperforming existing methods.
Author Chen, Hao
Lu, Jiaxin
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Snippet Steel plate fault detection plays a crucial role in industrial manufacturing. However, the inherent complexity of steel plate fault data and the redundancy of...
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SubjectTerms Accuracy
Algorithms
Aluminum
Artificial neural networks
Datasets
Fault detection
Feature extraction
Informatics
Machine learning
Manufacturing
Neural networks
Principal components analysis
Product reliability
Steel plates
Title Dual-Branch Residual Network for Enhanced Steel Plate Fault Detection
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