Steel Surface Defect Recognition: A Survey

Wen, Xin; Shan, Jvran; He, Yu; Song, Kun

doi:10.3390/coatings13010017

Cited by 61 publications

(26 citation statements)

References 123 publications

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“…The other are deep learning-based defect detection methods that have been researched and used widely [29], which are three main categories, including supervised methods, unsupervised methods, and weakly supervised methods. Many researchers focus on supervised approaches to establish end-to-end defect detection system [30]. For example, an end-to-end defect detection network (EDDN) was developed to handle defects with different scales [31].…”

Section: Steel Surface Defect Detectionmentioning

confidence: 99%

Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

Jiansheng

Wang

et al. 2023

IET Signal Processing

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Next-generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks-based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region-based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region-based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU-DET and GC10-DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted-surface, rolled-in scale and scratches on NEU-DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively.

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Section: Steel Surface Defect Detectionmentioning

confidence: 99%

Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

Jiansheng

Wang

et al. 2023

IET Signal Processing

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“…Wen et al. [ 39 ] discussed the key hardware of steel surface defect detection systems and offered suggestions for related options. Dong et al.…”

Section: Related Workmentioning

confidence: 99%

“…[37] Luo et al [38] presented a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of concasting slabs and hot-and cold-rolled steel strips. Wen et al [39] discussed the key hardware of steel surface defect detection systems and offered suggestions for related options. Dong et al [10] proposed a pixel-level surface defect detection model based on a pyramid feature fusion module to enable effective fusion of multi-scale feature maps generated by the backbone network.…”

Section: Deep Learning In Industrymentioning

confidence: 99%

A One‐Stage Deep Learning Model for Industrial Defect Detection

Wei

Hassaballah

et al. 2023

Advcd Theory and Sims

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Industrial defect detection is a hot topic in the field of computer vision and industry. Industrial defects are diverse and complex, and well-known machine learning based methods can often not effectively extract features of industrial defects and achieve good detection results. To address the above problems, this paper introduces a deep learning model for industrial defect detection. First, a two-branch decoupled head, which can facilitate model training through separating the prediction of category and regression is designed. Also, two inverted bottleneck structures are designed to enhance the ability of the model to extract features. Moreover, an attention-enhanced feature fusion (AEFF) module is designed and integrated into the neck network to achieve effective feature fusion. Extensive experiments are conducted on three public datasets, namely the DeepPCB dataset, NEU-DET dataset, and NRSD-MN dataset. The obtained results demonstrate that the proposed model achieves competitive results compared to the state-of-the-art methods. The proposed model achieves mAP@0.5:0.95, 71.78%, 36.04%, and 48.69% on the PCB dataset, NEU-DET dataset, and the NRSD-MN dataset, respectively.

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“…In recent years, the application of advanced computer vision technologies to enhance the accuracy and efficiency of defect detection has become a research focus, fueled by the development of deep learning technologies. Despite the significant achievements of deep learning in image processing, its application in steel surface defect detection faces certain challenges [2] [3]. Firstly, these algorithms typically rely on large volumes of labeled data for training, but acquiring a substantial quantity of high-quality steel surface defect samples is both challenging and costly in industrial settings.…”

Section: Introductionmentioning

confidence: 99%

Multi-level supervised vision language model based steel surface defect detection

Tan,

Shan,

Wang

2024

Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023)

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Steel Surface Defect Recognition: A Survey

Cited by 61 publications

References 123 publications

Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

A One‐Stage Deep Learning Model for Industrial Defect Detection

Multi-level supervised vision language model based steel surface defect detection

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