Gap Detection of Switch Machines in Complex Environment Based on Object Detection and Image Processing

Tao, Ting; Dong, Dezun; Huang, Shize; Chen, Wei

doi:10.1061/jtepbs.0000406

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…Numerous research studies have demonstrated that adversarial examples pose threats to other image recognition domains 27 . In the object detector domain, 28 some researchers studied adversarial example generation methods against R-CNN series 29 . Wei et al 30 .…”

Section: Related Workmentioning

confidence: 99%

Adversarial attack on human pose estimation network

Zhang,

Huang,

Liu

et al. 2024

J. Electron. Imag.

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Real-time human pose estimation (HPE) using convolutional neural networks (CNN) is critical for enabling machines to better understand human beings based on images and videos, and for assisting supervisors in identifying human behavior. However, CNN-based systems are susceptible to adversarial attacks, and the attacks specifically targeting HPE have received little attention. We present a gradient-based adversarial example generation method, named AdaptiveFool, which is designed to effectively perform a keypoints-invisible attack against OpenPose by aggregating the loss function of human keypoints and generating adaptive adversarial perturbations. In addition, we introduce an object-oriented perturbation generation method during the AdaptiveFool process to eliminate background perturbations. Our proposed method adapts the adversarial perturbations and generates object-oriented perturbations. On COCO 2017 datasets, our method achieves 6.3% mean average precision on OpenPose. This research provides inspiration for future work on developing efficient and effective adversarial example defense methods for HPE.

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Section: Related Workmentioning

confidence: 99%

Adversarial attack on human pose estimation network

Zhang,

Huang,

Liu

et al. 2024

J. Electron. Imag.

Self Cite

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“…The Grab Cut algorithm is used [13] with the combination of deep neural network to provide the skin lesion segmentation. A YOLObased object detection architecture in combination with image processing algorithm has been explained in the study of Tao et al [14].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

Vengaloor¹,

Muralidhar²

2022

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Automatic detection of anomalies on the metal surface is an essential capability in industries to provide the better-quality control. To locate and identify the type of defect, it is necessary to find the Region of interest (RoI) from the captured image. Segmentation of the captured image is one among the many methods to achieve this task. Therefore, a precise and accurate segmentation method has major role to improve the metal surface anomaly detection rate in industry. As the defects are different in it’s size, shape and type, the process of semantic segmentation for metal surface is considered as a challenging task. To address this issue, a deep learning based high calibre U- shaped network is proposed. It can be considered as an automatic quality control system for industries. The proposed method is effective in predicting the presence of defects. The system is also capable to locate the position of the defect on surface without the intervention of human being. The up-sampling technique provided with the convolutional neural network in the architecture makes the system to produce high resolution outputs. The proposed system has been evaluated based on accuracy, precision, loss and IoU after training and testing the model using two different datasets called NEU metal surface defect database and Kolektor surface defect data set.

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“…Recent intelligent sensor advancements have contributed to data-driven PHM research on railway turnout. The related research contents include gap measurement [10][11][12], electric power analysis [13][14][15], electric current analysis [16][17][18], and sound analysis [19][20][21]. These studies are highly dependent on features, and they are divided into three categories according to the way features are constructed: the manual features applied in classifiers approach [22,23], the distance-based measurement approach [14,24], and the automatic features with deep learning approach [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

Chen¹,

Li²,

Huang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train's safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a twodimensional image by data pre-processing and 2D representation. Next, a binary classification model based on the convolutional autoencoder is developed to implement fault detection. The profile and structure information can be captured by processing data as images. The performance of our method is evaluated and tested on real-world operational current data in the metro stations. Experimental results show that the proposed method achieves better performance, especially in terms of error rate and specificity, and is robust in practical engineering applications.

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Gap Detection of Switch Machines in Complex Environment Based on Object Detection and Image Processing

Cited by 19 publications

References 11 publications

Adversarial attack on human pose estimation network

Adversarial attack on human pose estimation network

Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

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