X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

• Published in: Symmetry (Basel) Vol. 13; no. 4; p. 706
• Main Authors: Feng, Xinglong, Gao, Xianwen, Luo, Ling
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2021
• Subjects: Accuracy; Algorithms; Artificial neural networks; Benchmarks; Cameras; Classification; convolutional neural network (CNN); Datasets; Defects; hot rolled steel strip defect; Hot rolling; Hot-rolled steel; Machine learning; Metal fatigue; Metal strips; RepVGG; spatial attention; Strip steel; surface defect dataset; Surface defects; Vision systems; Workloads
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym13040706

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.