Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks

Tao, Xu; Wang, Zihao; Zhang, Zhengtao; Zhang, Dapeng; Xu, De; Gong, Xiaoyan; Zhang, Lei

doi:10.1109/tcpmt.2018.2794540

Cited by 60 publications

(20 citation statements)

References 29 publications

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“…On a coarse-grained dataset, Taylor L et al used six data augmentation methods that include clipping, rotation, flipping, color jittering, edge enhancement, and fancy Principal Components Analysis (PCA) [ 22 ]. For wire defect recognition, Tao X et al investigated four data augmentation methods, and rotation was deemed most effective method [ 23 ]. On Common Objects in Context (MS COCO) dataset, Kisantal M et al proposed a data augmentation method to improve the accuracy by oversampling images with small objects [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…This process is proved to benefit the model in generalization ability and robustness [ 20 ]. By various data augmentation methods on images, such as random cropping, rotation, radiation transformation, and Gaussian noise, the limited samples are more fully used to train better models for practices [ 21 , 22 , 23 ]. Furthermore, the best data augmentation strategy is dataset-specific.…”

Section: Introductionmentioning

confidence: 99%

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CNN Training with Twenty Samples for Crack Detection via Data Augmentation

Wang¹,

Yang²,

Jiang³

et al. 2020

Sensors

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The excellent generalization ability of deep learning methods, e.g., convolutional neural networks (CNNs), depends on a large amount of training data, which is difficult to obtain in industrial practices. Data augmentation is regarded commonly as an effective strategy to address this problem. In this paper, we attempt to construct a crack detector based on CNN with twenty images via a two-stage data augmentation method. In detail, nine data augmentation methods are compared for crack detection in the model training, respectively. As a result, the rotation method outperforms these methods for augmentation, and by an in-depth exploration of the rotation method, the performance of the detector is further improved. Furthermore, data augmentation is also applied in the inference process to improve the recall of trained models. The identical object has more chances to be detected in the series of augmented images. This trick is essentially a performance–resource trade-off. For more improvement with limited resources, the greedy algorithm is adopted for searching a better combination of data augmentation. The results show that the crack detectors trained on the small dataset are significantly improved via the proposed two-stage data augmentation. Specifically, using 20 images for training, recall in detecting the cracks achieves 96% and Fext(0.8), which is a variant of F-score for crack detection, achieves 91.18%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

Wang¹,

Yang²,

Jiang³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Deep-learning technology has developed rapidly and made great success in object detection [ 61 ], intelligent robot [ 62 ], saliency detection [ 63 ], parking garage sound event detection [ 64 ], sound event detection for smart city safety [ 65 , 66 ], UAV blade fault diagnosis [ 67 , 68 , 69 ] and other fields [ 70 , 71 , 72 ]. Deep learning has a kind of deep neural network structure with multiple convolutions layer.…”

Section: Survey Of Deep-learning Defect-detection Technologiesmentioning

confidence: 99%

“…Deep learning has a kind of deep neural network structure with multiple convolutions layer. By combining low-level features to form a more abstract high-level representation of attribute categories or features, the data can be better reached in abstract ways such as edge and shape to improve the effectiveness of the deep-learning algorithm [ 70 ], Therefore, many researchers try to use deep-learning technology to defect detection of product and improved the product quality [ 71 , 72 , 73 , 74 ]. Table 2 summarizes the advantages and disadvantages of deep-learning methods commonly used in product defect detection.…”

Section: Survey Of Deep-learning Defect-detection Technologiesmentioning

confidence: 99%

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

et al. 2020

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The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

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“…They established a convolutional neural network to extract defect features from a suspicious area and, finally, the accuracy of detection was more than 96% [21]. S. Nahavand et al used intelligent algorithms to detect defects on a metal surface [22]; Xian Tao et al developed a machine vision device to detect defects on an electrical connector using convolutional neural networks, and they discussed the effects of data augmentation on defect recognition [23]; Yuan et al used a modified segmentation method and deep neural networks to detect defects on the cover glass of mobile phones, and used GAN to generate new data in order to overcome the drawbacks presented when a huge amount of data is unavailable [24].…”

mentioning

confidence: 99%

Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks

Wen

Chen

2018

Applied Sciences

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Bearings are commonly used machine elements and an important part of mechanical transmission. They are widely used in automobiles, airplanes, and various instruments and equipment. Bearing rollers are the most important components in a bearing and determine the performance, life, and stability of the bearing. In order to control the surface quality of the rollers, a machine vision system for bearing roller surface inspection is proposed. We briefly introduced the design of the machine vision system and then focused on the surface inspection algorithm. We proposed a multi-task convolutional neural network to detect defects. We extracted the features of the defects through a shared convolutional neural network, then classified the defects and calculated the position of the defects simultaneously. Finally, we determined if the bearing roller was qualified according to the position, category, and area of the defect. In addition, we explored various factors affecting performance and conducted a large number of experiments. We compared our method with the traditional methods and proved that our method had good stability and robustness.

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Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks

Cited by 60 publications

References 29 publications

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges

Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks

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