A cascaded combination method for defect detection of metal gear end-face

Su, Yingtao; Yan, Ping; Yi, Runzhong; Chen, Jian; Hu, Jinghua; Wen, Chao

doi:10.1016/j.jmsy.2022.05.001

Cited by 28 publications

(8 citation statements)

References 20 publications

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“…They transformed the images into grayscale after preprocessing and utilized the Otsu method to establish a threshold range to improve the accuracy of crack detection. Su et al [4] proposed a method for extracting images based on visual saliency regions in order to obtain more effective features. Based on FT [5], the method utilized Otsu thresholding to eliminate interference from non-detection areas, ineffective features, and edge burrs in the detection results.…”

Section: Traditional Image Processing Methodsmentioning

confidence: 99%

“…(3) Furthermore, considering that defects typically appear in only a few pixel areas (usually less than 2x2mm) within high-resolution images, reliance on a high-resolution detection environment is necessary for conducting the inspection. (4) it is challenging to determining the optimal hyperparameters for trained models, due to the fact that most deep learning models resemble black boxes. Their lack of interpretability necessitates and manual parameter tuning, resulting in high trial-and-error costs.…”

Section: Introductionmentioning

confidence: 99%

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Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Yang,

Zhou,

Wang

et al. 2023

Preprint

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Some common problems, including the effect of non-detection regions on accuracy, the small size and multi-scale of defects,and the challenge of automatically optimizing neural network hyperparameters, are confronted during the metal gear end-face defect detection, lead to the inadequate performance of accuracy and efficiency, making them unsuitable for meeting the real-time online detection demands in industries. To address the problems above, this study proposes a method SF-YOLONet to detect defects on metal gear end faces by using the Optimized Evolutionary Algorithm. Firstly, a testing platform was constructed to detect surface defects on metal gear end-faces. Subsequently, in order to address the impact of non-detection regions on accuracy, this study introduces the SF algorithm, a visual saliency-based image extraction method, to eliminates interference between ineffective features in non-detection regions and edge burrs. Additionally, A network (YOLONet) for detecting end-face defects in metal gears is introduced, which integrates the CBAM module and BiFPN feature extraction strategy. These advancements enhance adaptive learning and feature extraction for small-sized defects on gear end-face, and combine low-resolution and deep-level semantic information, to detect small and multi-scale defects is enhanced. Finally, the ISSA algorithm is introduced to optimize the hyperparameters of the SF-YOLONet model, thereby avoiding the instability of manual parameterization. The experiment demonstrated that the SF-YOLONet model achieved an average precision of 98.01% and an F1 score of 0.99 on the Metal Gear end-face defect testing dataset. The average computation time for detection per image on the YOLONet model was 0.13 seconds. Compared to other deep learning models, the proposed SF-YOLONet model significantly improves precision and efficiency in detecting defects on gear end-face. This enhancement effectively meets the real-time online detection requirements of industries.

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Section: Traditional Image Processing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Yang,

Zhou,

Wang

et al. 2023

Preprint

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“…Tang et al [21] proposed a defect detection method based on an attention mechanism and multi-scale maxpooling, which effectively improved the accuracy of strip steel surface defect detection. Su et al [22] proposed a cascade combination method SR-ResNetYOLO for automatic defect detection by multi-scale fusion of region extraction and 16× downsampling features, which can effectively detect small-size and multi-scale defects. Liu et al [23] proposed a new neural network called inception dual network to solve the problem of steel defect detection.…”

Section: Related Workmentioning

confidence: 99%

Detection algorithm for bearing roller end surface defects based on improved YOLOv5n and image fusion

Xie¹,

Zhu²,

Luo³

et al. 2023

Meas. Sci. Technol.

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For the current problems of low accuracy and poor reliability of defect detection of bearing roller end surfaces in industrial production, this paper proposes a bearing roller end surface defect detection algorithm based on improved YOLOv5n and the fusion of gamma-corrected maps and curvature maps. First, this paper uses photometric stereo to reconstruct the three-dimensional shape of the surface and proposes an improved Frankot-Chellappa integration algorithm to solve the problem of reconstructing surface deformation. Secondly, the DenseFuse network is used to fuse gamma-corrected maps and curvature maps to generate an image dataset that combines the strengths of both images to enhance defect features and improve the precision of target detection. Finally, the improved target detection model YOLOv5n is proposed to detect defects in the end surfaces of bearing rollers. The experimental results show that using fused images for training, detection models with higher mAP than traditional images can be obtained, and the improved YOLOv5n algorithm maintains the high real-time performance of the original algorithm while the mean average precision mAP0.5 and mAP0.5:0.95 of improved YOLOv5n are 98.6% and 87.4% respectively, which are 0.9% and 2.8% higher than YOLOv5n respectively.

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“…Deep learning significantly reduces labor costs and improves precision in image classification, 19 semantic segmentation, 20 target detection, 21 and other fields 22 . The YOLO series algorithm is widely used and recognized as an effective defect detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

Ding,

Pan,

Zhang

et al. 2023

J. Electron. Imag.

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.Metal gaskets are an essential component of the manufacturing industry, and detecting end-face defects is critical to ensuring their quality. However, current detection methods struggle to detect such defects due to the gaskets’ uneven end-face structure and small defect size. To address such issues, an approach called SR-HorNetYOLO has been proposed. This approach combines traditional machine vision and deep learning techniques. The visual saliency method is used to simplify the image and remove irrelevant features. YOLOv5 is then optimized in three ways: the GhostNet backbone replaces the CSPDarkNet53 backbone to reduce model weight, the self-attention mechanism and bottleneck transformer block improve the precision of detecting small targets, and the feature pyramid is improved through the HorNet network to enhance the predictive performance of the network. Finally, a classification module and position module are employed to classify and locate three-fusion proportion characteristic images. The experimental results show that the SR-HorNetYOLO method achieves a precision of 93.3% and a recall rate of 94.0%.

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A cascaded combination method for defect detection of metal gear end-face

Cited by 28 publications

References 20 publications

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Detection algorithm for bearing roller end surface defects based on improved YOLOv5n and image fusion

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

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