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

Abstract

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.

A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

“…In the next work, we may verify through more experiments which combination of this scheme and which model will achieve optimal results. Although our previous paper [7] showed that a RepVGG based scheme with an added attention mechanism may be superior in terms of effectiveness; according to Table 3, the ResNet50 based approach has an advantage over RepVGG in terms of number of parameters and computational complexity, i.e., it is easier to deploy in practice.…”

“…With the definition of channel attention and DCT, we can summarize two points: (1) Existing methods use GAP as preprocessing when doing channel attention. (2) DCT can be viewed as a weighted sum of inputs, and the weights are the cosine part of Equations ( 6) and (7). For more details, please refer to the reference [29].…”

“…We choose the newly proposed X-SDD [7] strip surface defect dataset to validate the proposed method in this paper. The X-SDD dataset contains 7 types of 1360 surface defects in hot rolled strip: 238 slag inclusions, 397 red iron sheet, 122 iron sheet ash, 134 surface scratches, 63 oxide scale of plate system, 203 finishing roll printing and 203 oxide scale of temperature system.…”

“…We validate the proposed algorithm on the X-SDD dataset [7], compare it with several deep learning models, and design ablation experiments to verify the effectiveness of the algorithm.…”

Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis

“…At present, optical-digital control of the rolled strip surface is used in production [4,5]. However, new methods for obtaining and processing images have new requirements for detecting and localizing defects and determining their size, shape, and potential hazard, in order to identify which rolled strips should be rejected [6]. One of the problems is identifying and classifying a wide range of defects that are similar in their geometry [7].…”