X-SDD

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 fifinal product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classifification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some diffificulties, 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.

准确分类热轧钢带缺陷至关重要，因为热轧钢带的缺陷检测与最终产品质量紧密相关。当前实际可用的热轧钢带缺陷数据集匮乏，在一定程度上限制了热轧钢带缺陷分类方向的进一步研究。在实际生产场景中，基于卷积神经网络（Convolutional Neural Network, CNN）的算法存在诸多局限，例如针对部分罕见缺陷的分类准确率欠佳。因此，如何将深度学习技术应用于热轧钢带表面缺陷的实际检测，仍有待进一步探索。本文提出了一款名为Xsteel表面缺陷数据集（X-SDD）的热轧钢带缺陷数据集，该数据集涵盖7种典型热轧钢带缺陷类型，总计包含1360张缺陷图像。相较于当前常用的NEU表面缺陷数据库（NEU-CLS）的6种缺陷类别，本文所提出的X-SDD包含更多的缺陷类型。随后，我们采用新近提出的RepVGG算法，并结合空间注意力（Spatial Attention, SA）机制，在X-SDD上对该算法的效果进行验证。最后，我们使用多种算法在我们提出的X-SDD上开展测试，以提供相应的基准测试结果。测试结果显示，所提算法在测试集上的分类准确率达到95.10%，大幅领先其他同类算法；同时，该算法在宏精确率、宏召回率以及宏F1分数三项指标上均取得了最优表现。

搜集汇总

背景与挑战

背景概述

X-SDD是一个专注于热轧钢带表面缺陷检测的数据集，包含七种典型缺陷类型，总计1360张图像，用于支持深度学习算法的研究和基准测试。与常用数据集相比，它提供了更丰富的缺陷类别，并通过RepVGG等算法验证了高效性能，测试准确率达到95.10%。