Defect Detection Method for Electric Multiple Units Key Components Based on Deep Learning

Zhao, Bing; Dai, Mingrui; Li, Ping; Xue, Rui; Ma, Xiang

doi:10.1109/access.2020.3009654

Cited by 14 publications

(12 citation statements)

References 34 publications

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“…However, the paper does not mention the effect of the model in practical application, such as whether the detection speed meets the requirements. Zhao et al 20 proposed a multi-defect detection pipeline and applied it to Fuxing Electric Multiple Units. It improves the anchor and feature fusion mechanism of Region Proposal Network (RPN) and combines the super-resolution strategy with CNN in the classification stage to improve classification performance.…”

Section: Deep Learning Methods For Defect Detectionmentioning

confidence: 99%

EfficientDet for fabric defect detection based on edge computing

Song

Jing

Huang

et al. 2021

Journal of Engineered Fibers and Fabrics

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The productivity of textile industry is positively correlated with the efficiency of fabric defect detection. Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods. Nonetheless, these cloud computing-based methods are still suboptimal due to the data transmission latency between the end devices and the cloud. To facilitate defect detection with more efficiency, a low-latency, low power consumption, easy upgrade, and automatical visual inspection system with the help of edge computing are proposed in this work. Firstly, the method uses EfficientDet-D0 as the detection algorithm, integrating the advantages of lightweight and scalable and can suit the resource-constrained edge device. Secondly, we performed data augmentations on five fabric datasets and verified the adaptability of the model in different types of fabrics. Finally, we transplanted the trained model to the edge device NVIDIA Jetson TX2 and optimized the model with TensorRT to make it detection faster. The performance of the proposed method is evaluated in five fabric datasets. The detection speed is up to 22.7 frame per second (FPS) on the edge device Jetson TX2. Compared with the cloud-based method, the response time is reduced by 2.5 times, with the capability of real-time industrial defect detection.

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Section: Deep Learning Methods For Defect Detectionmentioning

confidence: 99%

EfficientDet for fabric defect detection based on edge computing

Song

Jing

Huang

et al. 2021

Journal of Engineered Fibers and Fabrics

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show abstract

“…EMU Train Key Components [149], [150] Others [151] model for the detection of brace sleeve screws, however, it is not clear whether the dataset is available. The same issue applies to the PAC-TPL2020 dataset used in [122].…”

Section: E Datasetsmentioning

confidence: 99%

“…With the term EMU Train key components, the authors of [149] and [150] referred to brake disk, brake caliper, tractor, side suspension, under suspension, and plate bolt. The authors proposed a system able to cope with EMU components defect detection inspired by the Faster R-CNN architecture with a feature extractor based on ResNet-101 (instead of VGG16).…”

Section: B Emu Train Key Componentsmentioning

confidence: 99%

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

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“…The idea of CoordConv is introduced here, and two additional channels are added to the input feature map. These two channels carry coordinate information, which allows the network to learn complete translation invariance and change translation correlation [40]. CoordConv is used at the VOLUME XX, 2017 "diamond" position in Figure 2, and its implementation principle is shown in Figure 5.…”

Section: Figure 4 Spp Structure Diagrammentioning

confidence: 99%

Research on a Product Quality Monitoring Method Based on Multi Scale PP-YOLO

Huang

Chen

et al. 2021

IEEE Access

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To monitor product quality in the production process in real time, this thesis proposes a quality monitoring model based on PaddlePaddle You Only Look Once (PP-YOLO). First, in the preprocessing stage, the data enhancement method and the K-means++ method are used to improve the robustness of the algorithm, and the generated anchor box can screen more refined features earlier. Second, ResNet50-vd with the deformable convolution idea is selected as the backbone of the detection model, the feature pyramid network structure and the composition of the loss function are improved, and the feature learning ability of the model is enhanced to enable it to detect multiple scales of defects. Finally, pruning is performed on the basis of the trained model to reduce the number of model parameters so that it can be deployed in industrial scenarios with limited hardware conditions. Experimental results show that the proposed quality monitoring model can meet the requirements for detection speed and accuracy in actual production, providing a new concept for the deployment of deep learning models in the industrial field.

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Defect Detection Method for Electric Multiple Units Key Components Based on Deep Learning

Cited by 14 publications

References 34 publications

EfficientDet for fabric defect detection based on edge computing

EfficientDet for fabric defect detection based on edge computing

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

Research on a Product Quality Monitoring Method Based on Multi Scale PP-YOLO

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