Deep Learning Architecture for Computer Vision-based Structural Defect Detection

Yang, Ruoyu; Singh, Shubhendu Kumar; Tavakkoli, Mostafa; Karami, M. Amin; Rai, Rahul

doi:10.1007/s10489-023-04654-w

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…At present, the neural networks commonly used in target detection are R-CNN, Fast R-CNN and YOLO [12,13]. Deep learning can quickly realize the target detection of new images with good detection effect, and has the following characteristics [14]: (1) It requires a large number of sample data to train the network (the deeper the level of the network structure, the more data sets need to be trained), but it is difficult to obtain enough defect data sets. (2) High hardware requirements.…”

Section: Introductionmentioning

confidence: 99%

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

Wu,

Luo,

Sun

et al. 2024

Sensors

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Aiming at the problems of the poor robustness and universality of traditional contour matching algorithms in engineering applications, a method for improving the surface defect detection of industrial products based on contour matching algorithms is detailed in this paper. Based on the image pyramid optimization method, a three-level matching method is designed, which can quickly obtain the candidate pose of the target contour at the top of the image pyramid, combining the integral graph and the integration graph acceleration strategy based on weak classification. It can quickly obtain the rough positioning and rough angle of the target contour, which greatly improves the performance of the algorithm. In addition, to solve the problem that a large number of duplicate candidate points will be generated when the target candidate points are expanded, a method to obtain the optimal candidate points in the neighborhood of the target candidate points is designed, which can guarantee the matching accuracy and greatly reduce the calculation amount. In order to verify the effectiveness of the algorithm, functional test experiments were designed for template building function and contour matching function, including uniform illumination condition, nonlinear condition and contour matching detection under different conditions. The results show that: (1) Under uniform illumination conditions, the detection accuracy can be maintained at about 93%. (2) Under nonlinear illumination conditions, the detection accuracy can be maintained at about 91.84%. (3) When there is an external interference source, there will be a false detection or no detection, and the overall defect detection rate remains above 94%. It is verified that the proposed method can meet the application requirements of common defect detection, and has good robustness and meets the expected functional requirements of the algorithm, providing a strong technical guarantee and data support for the design of embedded image sensors in the later stage.

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Section: Introductionmentioning

confidence: 99%

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

Wu,

Luo,

Sun

et al. 2024

Sensors

View full text Add to dashboard Cite

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Multi-stage Damage Identification of Elastically Restrained Plates Based on Singular Value Decomposition and Faster-RCNN

Jiang,

Du,

Liu

2024

Mechanisms and Machine Science

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Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs

Zhu,

Wang,

Chen

et al. 2024

Complex Intell. Syst.

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High-quality printed circuit boards (PCBs) are essential components in modern electronic circuits. Nevertheless, most of the existing methods for PCB surface defect detection neglect the fact that PCB surface defects in complex backgrounds are prone to long-tailed data distributions, which in turn affects the effectiveness of defect detection. Additionally, most of the existing methods ignore the intra-scale features of defects and do not utilize auxiliary supervision strategies to improve the detection performance of the network. To tackle these issues, we propose a lightweight long-tailed data mining network (LLM-Net) for identifying PCB surface defects. Firstly, the proposed Efficient Feature Fusion Network (EFFNet) is applied to embed intra-scale feature associations and multi-scale features of defects into LLM-Net. Next, an auxiliary supervision method with a soft label assignment strategy is designed to help LLM-Net learn more accurate defect features. Finally, the issue of inadequate tail data detection is addressed by employing the devised Binary Cross-Entropy Loss Rank Mining method (BCE-LRM) to identify challenging samples. The performance of LLM-Net was evaluated on a homemade dataset of PCB surface soldering defects, and the results show that LLM-Net achieves the best accuracy of mAP@0.5 for the evaluation metric of the COCO dataset, and it has a real-time inference speed of 188 frames per second (FPS).

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Deep Learning Architecture for Computer Vision-based Structural Defect Detection

Cited by 5 publications

References 32 publications

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

An Improvement Method for Improving the Surface Defect Detection of Industrial Products Based on Contour Matching Algorithms

Multi-stage Damage Identification of Elastically Restrained Plates Based on Singular Value Decomposition and Faster-RCNN

Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs

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