Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects

Abstract

Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tasks in the steel industry. In this paper, we propose a deep learning-based model to classify six of the most common steel strip surface defects using the NEU-CLS dataset. We investigate the effectiveness of two state-of-the-art CNN architectures (MobileNet-V2 and Xception) combined with the transfer learning approach. The proposed approach uses an ensemble of two pre-trained state-of-the-art Convolutional Neural Networks, which are MobileNet-V2 and Xception. To perform a comparative analysis of the proposed architectures, several evaluation metrics are adopted, including loss, accuracy, precision, recall, F1-score, and execution time. The experimental results show that the proposed deep ensemble learning approach provides higher performance achieving an accuracy of 99.72% compared to MobileNet-V2 (98.61%) and Xception (99.17%) while preserving fast execution time and small models' size.

A zero-shot learning for property prediction of wear-resistant steel based on Multiple-source

“…To validate the proposed multi-source based TCA, the model of SVR is established after transfer and trained by several cross-transfer task, and the predicted results of training set with S 1 and S 3 as source domains and S 2 as target domain are illustrated in figure 4. In previous literature, modeling of transfer learning was based on a single source [8][9][10][11][12][13][14]. Therefore, a single-source cross-transfer experiment was performed to compare with the method proposed in this paper.…”

“…Hao et al [8] designed the hot zone of G8 ingot furnace by using the hot zone evaluation parameters and geometric parameters of that of G7 ingot furnace, and successfully transferred the growth law of ingot crystalline silicon from small-size crystals to large-size ones. Bouguettaya et al [9] classified six common defects of strip surface based on the deep learning model taking the NEU-CLS as the source domain, and the classification accuracy was higher than 98%. And the classification accuracy could be further improved by adding the data set with fewer unbalance defect samples to the NEU-CLS [10].…”

A Deep Learning‐Based Approach to the Estimation of Jominy Profile of Medium‐Carbon Quench Hardenable Steels

“…[54] Instead of training a model from scratch on a specific task, TL leverages the knowledge gained from solving a task to improve performance on a different one. TL has been successfully applied in various domains and applications also for the steel sector, such as image analysis, [55][56][57][58] natural language processing, [59] and speech recognition. [60] It allows models to benefit from previous knowledge and accelerate the development and deployment of ML systems.…”

RDDPA: Real-time Defect Detection via Pruning Algorithm on Steel Surface

“…Currently, there is a plethora of outstanding research in the field of hot rolled strip surface defect recognition, with increasing accuracy [1][2][3] . However, the simple classification algorithm cannot locate the specific location of the defect, nor can it judge the size of the defect, which is not conducive to the statistical analysis of the defect by the factory.…”