Unsupervised Pixel-Level Detection of Rail Surface Defects Using Multistep Domain Adaptation

Zhang, Zhaoxiang; Zhang, Limao

doi:10.1109/tsmc.2023.3274878

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…With the development of computer technology, researchers apply advanced artificial intelligence technology in the field of transportation, such as traffic sign detection in the field of road traffic [1,2], railway surface [3][4][5][6] and fastener [7,8] defect detection in the field of rail transit. With the rapid development of urban rail transit, the subway system has become the preferred mode of public transportation, which undoubtedly raises higher requirements for the safety of trains.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Subway Driver Action Sensoring and Detection Based on Lightweight ShuffleNetV2 Network

Shen,

Wei

2023

Sensors

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The driving operations of the subway system are of great significance in ensuring the safety of trains. There are several hand actions defined in the driving instructions that the driver must strictly execute while operating the train. The actions directly indicate whether equipment is normally operating. Therefore, it is important to automatically sense the region of the driver and detect the actions of the driver from surveillance cameras to determine whether they are carrying out the corresponding actions correctly or not. In this paper, a lightweight two-stage model for subway driver action sensoring and detection is proposed, consisting of a driver detection network to sense the region of the driver and an action recognition network to recognize the category of an action. The driver detection network adopts the pretrained MobileNetV2-SSDLite. The action recognition network employs an improved ShuffleNetV2, which incorporates a spatial enhanced module (SEM), improved shuffle units (ISUs), and shuffle attention modules (SAMs). SEM is used to enhance the feature maps after convolutional downsampling. ISU introduces a new branch to expand the receptive field of the network. SAM enables the model to focus on important channels and key spatial locations. Experimental results show that the proposed model outperforms 3D MobileNetV1, 3D MobileNetV3, SlowFast, SlowOnly, and SE-STAD models. Furthermore, a subway driver action sensoring and detection system based on a surveillance camera is built, which is composed of a video-reading module, main operation module, and result-displaying module. The system can perform action sensoring and detection from surveillance cameras directly. According to the runtime analysis, the system meets the requirements for real-time detection.

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Section: Introductionmentioning

confidence: 99%

A Real-Time Subway Driver Action Sensoring and Detection Based on Lightweight ShuffleNetV2 Network

Shen,

Wei

2023

Sensors

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Uncertainty inspired domain adaptation network for rail surface defect segmentation

Yan,

Ma,

Song

et al. 2024

Engineering Applications of Artificial Intelligence

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A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification

Ji,

Xue,

Zhang

et al. 2023

ECAM

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Purpose Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.Design/methodology/approach This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.Findings In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.Originality/value The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

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Unsupervised Pixel-Level Detection of Rail Surface Defects Using Multistep Domain Adaptation

Cited by 3 publications

References 41 publications

A Real-Time Subway Driver Action Sensoring and Detection Based on Lightweight ShuffleNetV2 Network

A Real-Time Subway Driver Action Sensoring and Detection Based on Lightweight ShuffleNetV2 Network

Uncertainty inspired domain adaptation network for rail surface defect segmentation

A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification

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