Detection of Various Types of Metal Surface Defects Based on Image Processing

Xue, Peng; Jiang, Changhong; Pang, Huanli

doi:10.18280/ts.380417

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Xue et al [19] proposed an image processing-based defect detection algorithm for various types of metal surfaces. It takes images of metal surfaces and applies average graying and non-local mean filtering before sending them to the SSD network for defect classification.…”

Section: Introductionmentioning

confidence: 99%

Lightweight YOLOv7 for bushing surface defects detection

Cheng,

Zeng,

Hao

2024

Preprint

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Bushings as metal structure parts because of their good welding performance, good plasticity strength, and other advantages in the engineering field are widely used. However, because the performance of the internal microstructure of the metal material is not uniform, there is an electrode potential difference between the micro area leading to surface corrosion; and the production process will inevitably produce defective surfaces with defective products, thus seriously affecting the subsequent use. Therefore, it is essential to accurately detect the defects on the surface of the bushing.At present, the inspection method based on machine vision has replaced the manual inspection method with low efficiency and a high false detection rate. However,due to the large amount of computation brought about by the complex network model, the efficiency can not meet the production needs of real-time detection; the simple network model is due to the limited ability to extract features and thus can not meet the requirements of the accuracy of the detection. To ensure the detection accuracy of the surface defects of the bushing and at the same time reduce the volume of the model, a bushing defect detection model based on the improved YOLOv7 is proposed. The backbone network of the model uses MobileNetv3 to replace the backbone network of the original YOLOv7, which improves the detection speed while guaranteeing the detection accuracy, and realizes the lightweight model at the same time; the CBAM (Convolutional Block Attention Module) attention mechanism is introduced into the residual edges of each layer of the backbone network, which pays more attention to the small-size target to get more important feature information; BiFPN feature pyramid is used to optimize the detection effect by weighted fusion of multi-scale feature information. The experimental results show that the improved algorithm in this paper reduces the mAP by only 0.7 percent compared with the traditional YOLOv7 algorithm; however, the detection speed is increased by 29.4 percent, and the model volume is reduced by 29.9 percent, which effectively improves the detection accuracy and speed of all kinds of defects on the surface of the bushings, and it can be better adapted to the industrial detecting environment. Mathematics Subject Classification (2020) 5206050

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Section: Introductionmentioning

confidence: 99%

Lightweight YOLOv7 for bushing surface defects detection

Cheng,

Zeng,

Hao

2024

Preprint

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“…Wrinkles are a form of imperfection on fasteners caused by material displacement during the forging process of nuts in particular. Deformation, dents, wrinkles, scratches, fractures, rough surface, missing and misaligned threads on the fastener surface are all faults generated by processing [20]. Small and medium-sized industries are the largest manufacturers of these fasteners but the process of inspection is done manually, which requires a lot of people, money, and time.…”

Section: Introductionmentioning

confidence: 99%

Taguchi based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Kaur¹,

Chauhan²,

Aggarwal³

et al. 2022

Preprint

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Fasteners play a critical role in securing various parts of machinery. Deformations such as dents, cracks, and scratches on the surface of fasteners are caused by material properties and incorrect handling of equipment during production processes. As a result, quality control is required to ensure safe and reliable operations. The existing defect inspection method relies on manual examination, which consumes a significant amount of time, money, and other resources; also, accuracy cannot be guaranteed due to human error. Automatic defect detection systems have proven impactful over the manual inspection technique for defect analysis. However, computational techniques such as convolutional neural networks (CNN) and deep learning-based approaches are evolutionary methods. By carefully selecting the design parameter values, the full potential of CNN can be realised. Using Taguchi based design of experiments and analysis, an attempt has been made to develop a robust automatic system in this study. The dataset used to train the system has been created manually for M14 size nuts having two labeled classes: Defective and Non-defective. There are a total of 264 images in the dataset. The proposed sequential CNN comes up with a 96.3% validation accuracy, 0.277 validation loss at 0.001 learning rate.

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