High-precision steel plate corrosion rate monitoring model based on dilated convolution

Ruixin Gao; Yisong Ma; Hongxing Xu; Ben Niu; Kaixin Cen; Li Wang; Peng Liu

doi:10.1117/12.3049767

27 September 2024 High-precision steel plate corrosion rate monitoring model based on dilated convolution

Ruixin Gao, Yisong Ma, Hongxing Xu, Ben Niu, Kaixin Cen, Li Wang, Peng Liu

Proceedings Volume 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024); 1328407 (2024) https://doi.org/10.1117/12.3049767
Event: Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 2024, Hangzhou, China

Abstract

Steel plate corrosion causes huge economic losses to industrial manufacturing environments and may induce accidents. Therefore, monitoring the corrosion rate of steel plates is very important in the industrial field. However, the accuracy of traditional steel plate corrosion rate monitoring is difficult to meet the requirements of industrial sites. For this reason, it is particularly important to design a high accuracy steel plate corrosion rate monitoring model. In order to solve this problem, this paper proposes a high accuracy steel plate corrosion rate monitoring model based on Dilated convolution, named HDCNN. Starting from the fine-tuning paradigm of Dilated convolution, a new plug-and-play self-attention mechanism module is proposed, named JAM module, to help the model extract more effective information, thereby improving the monitoring accuracy of the model. At the same time, in order to verify the effectiveness of the model, this article self-made a set of data sets with multiple complex working conditions (different lights and different experimental scenarios), and conducted comparative experiments under the data sets with multiple complex working conditions. Experimental results show that among the three groups of mainstream models, the HDCNN model has the highest test accuracy (Accuracy: 93.1785 %).

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruixin Gao, Yisong Ma, Hongxing Xu, Ben Niu, Kaixin Cen, Li Wang, and Peng Liu "High-precision steel plate corrosion rate monitoring model based on dilated convolution", Proc. SPIE 13284, Third International Conference on Intelligent Mechanical and Human-Computer Interaction Technology (IHCIT 2024), 1328407 (27 September 2024); https://doi.org/10.1117/12.3049767

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KEYWORDS

Corrosion

Convolution

Data modeling

Feature extraction

Machine learning

Transformers

Education and training

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