A Double-Branch Surface Detection System for Armatures in Vibration Motors with Miniature Volume Based on ResNet-101 and FPN

Feng, Tao; Liu, Jiange; Xia, Fang; Wang, Jie; Zhou, Libin

doi:10.3390/s20082360

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…In addition, we also compare the proposed method with other deep learning models, including Faster R‐CNN [39], YOLOv3 [40], Inception‐v3 [41], ResNet‐50, ResNet‐101 [42], Ref [26], and Ref [43]. Furthermore, the effectiveness of the mixed dataset augmentation and the ensemble learning proposed in this study is also demonstrated by ablative analysis.…”

Section: Experimental Dataset and Analysis Resultsmentioning

confidence: 95%

Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

Peng

Liu

et al. 2022

High Voltage

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Automatically and accurately detecting the self‐blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open‐loop cognitive models to detect the self‐blast state of glass insulators, this study explores a mixed data augmentation‐based intelligent recognition method to detect the self‐blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high‐quality generative self‐blast samples of the glass insulator, and the non‐generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi‐scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi‐deformable convolutional network. Then, the extracted feature maps are transmitted to a two‐dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self‐blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self‐blast state of the glass insulator. Then, based on transfer learning and the established self‐optimising feedback mechanism for feature pyramid network, the self‐optimising adjustment and reconstruction of the feature map space with strong semantics and multi‐scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi‐channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.

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Section: Experimental Dataset and Analysis Resultsmentioning

confidence: 95%

Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

Peng

Liu

et al. 2022

High Voltage

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“…The deep network has a low resolution and learns semantic features. The higher the level, the greater the abstraction of the features, the smaller the size of the feature map, and small-sized objects are easily missed ( 15 ). The location and size of the gallbladder in this study were different, and the deep residual convolutional network was relatively easy to false alarms, making the detection results inaccurate and affecting the classification accuracy of the model.…”

Section: Discussionmentioning

confidence: 99%

Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?

Duan,

Yang,

Zhu

et al. 2024

Front. Med.

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BackgroundMany screening and diagnostic methods are currently available for biliary atresia (BA), but the early and accurate diagnosis of BA remains a challenge with existing methods. This study aimed to use deep learning algorithms to intelligently analyze the ultrasound image data, build a BA ultrasound intelligent diagnostic model based on the convolutional neural network, and realize an intelligent diagnosis of BA.MethodsA total of 4,887 gallbladder ultrasound images of infants with BA, non-BA hyperbilirubinemia, and healthy infants were collected. Two mask region convolutional neural network (Mask R-CNN) models based on different backbone feature extraction networks were constructed. The diagnostic performance between the two models was compared through good-quality images at the image level and the patient level. The diagnostic performance between the two models was compared through poor-quality images. The diagnostic performance of BA between the model and four pediatric radiologists was compared at the image level and the patient level.ResultsThe classification performance of BA in model 2 was slightly higher than that in model 1 in the test set, both at the image level and at the patient level, with a significant difference of p = 0.0365 and p = 0.0459, respectively. The classification accuracy of model 2 was slightly higher than that of model 1 in poor-quality images (88.3% vs. 86.4%), and the difference was not statistically significant (p = 0.560). The diagnostic performance of model 2 was similar to that of the two radiology experts at the image level, and the differences were not statistically significant. The diagnostic performance of model 2 in the test set was higher than that of the two radiology experts at the patient level (all p < 0.05).ConclusionThe performance of model 2 based on Mask R-CNN in the diagnosis of BA reached or even exceeded the level of pediatric radiology experts.

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“…The ResNet has been widely applied to the field of natural language processes and computer vision 22,23 . To the best of our knowledge, we do not find any approaches that apply ResNet to code smell detection.…”

Section: Code Smell Detectionmentioning

confidence: 93%

“…The ResNet has been widely applied to the field of natural language processes and computer vision. 22,23 To the best of our knowledge, we do not find any approaches that apply ResNet to code smell detection. The traditional CNN model can not extract deep feature information of code metrics, resulting in relatively low accuracy.…”

Section: Improved Resnetmentioning

confidence: 99%

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Zhang

Dong

2021

J Software Evolu Process

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Code smell is the structural design defect that makes programs difficult to understand, maintain, and evolve. Existing works of code smell detection mainly focus on prevalent code smells, such as feature envy, god class, and long method. Few works have been done on detecting brain class/method. Furthermore, existing deep‐learning‐based approaches leverage the CNN model to improve accuracy by barely increasing the number of layers, which may cause a problem of gradient degradation. To this end, this paper proposes a novel approach called MARS to detect brain class/method. MARS improves the gradient degradation by employing an improved residual network. It increases the weight value of those important code metrics to label smelly samples by introducing a metric–attention mechanism. To support the training of MARS, a dataset called BrainCode is generated by extracting more than 270,000 samples from 20 real‐world applications. MARS is evaluated on BrainCode and compared to other machine‐learning‐based and deep‐learning‐based approaches. The experimental results demonstrate that the average accuracy of MARS is 2.01 % higher than that of the existing approaches, which improves state‐of‐the‐art.

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A Double-Branch Surface Detection System for Armatures in Vibration Motors with Miniature Volume Based on ResNet-101 and FPN

Cited by 12 publications

References 25 publications

Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?

MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

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