Application of Multi-Scale Feature Fusion and Deep Learning in Detection of Steel Strip Surface Defect

Li, Kangyu; Wang, Xifeng; Ji, Lijuan

doi:10.1109/aiam48774.2019.00136

Cited by 22 publications

(8 citation statements)

References 12 publications

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“…Their experimental results demonstrated that the detection method based on deep learning is more effective than the traditional method and can detect the surface defects of the steel strips more accurately. Li [40] and Wei [41] employed improved Faster R-CNN to detect surface defects on steel strips, and improved detection accuracy by adopting multi-scale feature fusion or introducing weighted regions of interest.…”

Section: Related Workmentioning

confidence: 99%

Surface defect detection of steel strips based on classification priority YOLOv3-dense network

Zhang

Kang

et al. 2020

Ironmaking & Steelmaking

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The steel strip is an essential raw material in the machinery industry. Besides, the surface defects of the steel strip directly determine its performance. To achieve rapid and effective detection of the defects, a CP-YOLOv3-dense (classification priority YOLOv3 DenseNet) neural network was proposed in the present study. The model used YOLOv3 as basic network, implemented priority classification on the images, and then replaced the two residual network modules with two dense network modules. Therefore, the network can receive the multi-layer convolution features output by the dense connection block before making predictions, consequently enhancing the reuse and fusion of features. Finally, the six kinds of surface defects were detected by the improved network. According to the results, the detection precision of the CP-YOLOv3-dense network is 85.7%, the recall rate is 82.3%, the mean average precision is 82.73%, and the detection time of each image is 9.68 ms.

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Section: Related Workmentioning

confidence: 99%

Surface defect detection of steel strips based on classification priority YOLOv3-dense network

Zhang

Kang

et al. 2020

Ironmaking & Steelmaking

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“…Automatic measurement of helical flutes of end mills by computer vision using an optical machine is a current research issue. The most important techniques for image measurement in solving engineering problems are divided into the determination of focal zones and have been studied by image processing methods, such as binarization and edge detection algorithms [44][45][46], image clustering [47][48][49][50] or the above-mentioned methods of growing regions for segmentation [51,52].…”

Section: Introductionmentioning

confidence: 99%

An approach for recognizing surface defects in products with a complex helical shape

Pivkin,

Yazev,

Grechishnikov

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

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A key functional role is served by the helical surfaces of carbide end mills that can be manufactured during diamond grinding wheel. Localized changes in the form of the helical surface can be caused by abrasion, high pressure, and grinding wheel wear. Therefore, it is extremely important to measure the physical samples of products with a helical surface according to the criterion of profile accuracy, rake angle and core diameter. A specialized inspection machine in reflected light can be used to obtain images across the helical groove. Manually extracting a number of defects from photos takes time. Using defect recognition algorithms, effective and quick quality control of a ground helical surface can be established. As a result, effective surface quality control can be achieved in the machine tool industry. In this study, an innovative approach to determine a defect's shape and location as well as an algorithm for removing it are presented. Both of these approaches are integrated into the technological process used to manufacture products with helical surfaces. With the goal to recognized create suggestions for image analysis using different image levels, the suggested approach provides logically smoothing histograms and limiting contrast as an image pre-processing, based on an analysis of images with useful and faulty parts. Achieved successful extraction of areas of adhesive, diffusion, abrasion and chips from the image through post-processing. The article presents a new approach to recognizing adhesive and diffusion defects on the helical surface of a mill after grinding. When developing this approach, it was revealed that areas with alternating profile changes are most susceptible to the formation of defects under conditions of increased heating of the working area, and specialized inductors for searching for defects in localized areas according to the criterion of pixel brightness intensity were proposed.

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“…In the field of meta-learning, the problem of few-shot learning is solved by means of class alignment, domain attention distribution, and multisource data fusion. Li et al [15] used the multiscale information feature fusion method to re-extract the texture information of the bottom layer of the image to focus more attention upon the slender and easily overlooked defects of the strip steel surface, and the detection accuracy of this method reached 98.26%. Wang et al [16] fused the historical data of strip defects to track model faults, which can assist expert decision-making.…”

Section: Introductionmentioning

confidence: 99%

“…The method of [13] can combat uneven samples, but it ignores the feature extraction ability of backbone networks and the efficiency and deployment of the models. Therefore, inspired by references [14][15][16], this pa-per integrates fine-grained information extraction, data sample enhancement, unbalanced sample confrontation, and model generalization performance transfer.…”

Section: Introductionmentioning

confidence: 99%

Research on a Classification Method for Strip Steel Surface Defects Based on Knowledge Distillation and a Self-Adaptive Residual Shrinkage Network

Huang,

Song,

et al. 2023

Algorithms

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Different types of surface defects will occur during the production of strip steel. To ensure production quality, it is essential to classify these defects. Our research indicates that two main problems exist in the existing strip steel surface defect classification methods: (1) they cannot solve the problem of unbalanced data using few-shot in reality, (2) they cannot meet the requirement of online real-time classification. To solve the aforementioned problems, a relational knowledge distillation self-adaptive residual shrinkage network (RKD-SARSN) is presented in this work. First, the data enhancement strategy of Cycle GAN defective sample migration is designed. Second, the self-adaptive residual shrinkage network (SARSN) is intended as the backbone network for feature extraction. An adaptive loss function based on accuracy and geometric mean (Gmean) is proposed to solve the problem of unbalanced samples. Finally, a relational knowledge distillation model (RKD) is proposed, and the functions of GUI operation interface encapsulation are designed by combining image processing technology. SARSN is used as a teacher model, its generalization performance is transferred to the lightweight network ResNet34, and it is conveniently deployed as a student model. The results show that the proposed method can improve the deployment efficiency of the model and ensure the real-time performance of the classification algorithms. It is superior to other mainstream algorithms for fine-grained images with unbalanced data classification.

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Application of Multi-Scale Feature Fusion and Deep Learning in Detection of Steel Strip Surface Defect

Cited by 22 publications

References 12 publications

Surface defect detection of steel strips based on classification priority YOLOv3-dense network

Surface defect detection of steel strips based on classification priority YOLOv3-dense network

An approach for recognizing surface defects in products with a complex helical shape

Research on a Classification Method for Strip Steel Surface Defects Based on Knowledge Distillation and a Self-Adaptive Residual Shrinkage Network

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