Neural Network-Based Surface Corrosion Classification on Metal Articles

Ahuja, Sanjay Kumar; Shukla, Manoj Kumar; Ravulakollu, Kiran Kumar

doi:10.1007/978-981-33-4968-1_10

Cited by 5 publications

(10 citation statements)

References 25 publications

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“…While many authors have dealt with deep learning algorithms to deal with corrosion detection issues [18], [28]- [33], few scientists have focused on classifying corrosion types in different environments [10], [13], [19], [34], [35]. More importantly, no work has been done to classify corrosion types for ASTs, and using EfficientNet architectures.…”

Section: Discussion and Comparison With The State-of-the-artmentioning

confidence: 99%

“…The main limitation of both models compared to this research was the robustness of the model and the data scarcity. A VGG-16, pre-trained and customized network was optimized to improve the classification accuracy to obtain a 93.8% in the F1-score metric [19].…”

Section: Discussion and Comparison With The State-of-the-artmentioning

confidence: 99%

“…Despite the above studies, there is limited information regarding CNN models for corrosion classification in ASTs. The closest solved corrosion classification problems found the in literature are related to metallic corrosion [18], [19]. These works trained CNN models, one to classify between five types of metal corrosion with 563 images, reaching a 93.8% performance, based on a custom CNN model [19], while the other used corrosion detection for four types of failures relayed on Yolov3-tiny architecture to get a performance of 85% [18].…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks for external corrosion classification in industrial above-ground storage tanks

Alviz-Meza,

Peña-Ballesteros

2024

Preprint

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This paper explores EfficientNet smaller models for a multiclassification task of corrosion types in above-ground storage tanks through transfer learning and finetuning approaches. Data augmentation was used to increment the data an oil and gas company provided, reaching a dataset of around 5000 images. The images were stored in Google Drive and imported by Colab to obtain the models using TensorFlow and Keras. After the hyperparameters’ tuning a transfer learning model was selected and explored with fine tuning. The EfficientNetB0 model delivered from fine-tuning accomplished 94% performance. This work is the first attempt to deploy an artificial vision automatic tool for being implemented during tank inspection in the industrial sector. In a further development, this model can be coupled with one based on object detection for the remote identification of failures due to external corrosion during tank inspection; improving safety and reliability in the oil and gas industry.

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Section: Discussion and Comparison With The State-of-the-artmentioning

confidence: 99%

Section: Discussion and Comparison With The State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks for external corrosion classification in industrial above-ground storage tanks

Alviz-Meza,

Peña-Ballesteros

2024

Preprint

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“…In terms of recognition error rate, the VGG-Corrosion model proposed in this paper can achieve 90% accuracy, which verifies that the method can quickly and accurately recognize the grade of corroded steel quantitatively. Compared to previous research [26,35], this method can further classify the degree of corrosion and quantitatively describe it based on corrosion weight gains. (b) Transfer learning solved the problem of the lack of data to some extent, but a certain number of training samples is still necessary.…”

Section: Discussionmentioning

confidence: 99%

“…The VGG-Corrosion achieved an accuracy of 90.96%, which proved the superiority of the CNN-based method and the VGG-Corrosion model. Compared with [35], the research proposed in this paper further classified the degree of corrosion. Although the research in the literature [26] also involved these, it was only a qualitative description, whereas the research in this paper can quantitatively describe the degree of corrosion.…”

Section: Testingmentioning

confidence: 99%

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

Wang¹,

Shen²,

Wu³

et al. 2022

Meas. Sci. Technol.

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For the maintenance of weathering steel structure facilities, it is necessary to evaluate the corrosion grade of the rust layer on the surface regularly. At present, the corrosion grade classification of weathering steel is mainly based on the human-eye inspection. In this paper, a deep learning method using a convolutional neural network for evaluating the corrosion grade of weathering steel is proposed to save time and manpower. Firstly, the image dataset of the corrosion steel plate was established using salt spray tests. Then, a CNN architecture named VGG-Corrosion was designed to evaluate the corrosion grade of the corroded steel plate. The effect of the learning rate, transfer learning, and batch size was also investigated to clarify the best hyperparameter configurations to train a powerful corrosion grade classification model. Under the best combination of considered hyperparameters, the mean average accuracy for the corrosion grade evaluation of the test results is 90.96%. The testing results indicated that the CNN based corrosion grade recognition for weath-ering steel plate is prospective, which would be helpful for safety evaluation of steel structures.

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Deep neural networks for external corrosion classification in industrial above-ground storage tanks

Alviz-Meza,

Hadechini-Meza,

Peña-Ballesteros

2024

Heliyon

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Neural Network-Based Surface Corrosion Classification on Metal Articles

Cited by 5 publications

References 25 publications

Deep Neural Networks for external corrosion classification in industrial above-ground storage tanks

Deep Neural Networks for external corrosion classification in industrial above-ground storage tanks

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

Deep neural networks for external corrosion classification in industrial above-ground storage tanks

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