An Online Metro Train Bottom Monitoring System Based on Multicamera Fusion

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2024-07-26 DOI:10.1109/JSEN.2024.3426553

Zhenyu Zhang;Jiabing Zhang;Yuejian Chen

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引用次数: 0

Abstract

The structure of the train bottom is relatively complex and has many small components. The failure of train bottom will threaten the safety of passengers, and train bottom monitoring is important for the safety of train operation. Thus, an online metro train bottom monitoring system based on multicamera fusion is developed. First, the linear array cameras are used to collect the images, effectively overcoming the problems of distortion and repeated captures. Then, an adaptive image correction method is introduced to correct the underexposed and overexposed images. The image-stitching method based on scale-invariant feature transform (SIFT) feature image registration is used to concatenate the train bottom images. Finally, the developed monitoring system is applied in Guangzhou Metro Line 21. The results show that the developed correction method effectively corrects the underexposed and overexposed images. The feature matching is performed after determining the overlap areas, which reduces the number of iterations and improves the stitching speed of the system. Compared with the existing method, the stitched images have higher quality in peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and difference of edge map (DoEM).

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基于多摄像头融合的地铁列车底部在线监控系统

列车底部结构相对复杂，有许多小部件。列车底部的故障会威胁到乘客的安全，因此列车底部监控对列车运行安全具有重要意义。因此，开发了基于多摄像头融合的地铁列车底部在线监测系统。首先，采用线性阵列摄像机采集图像，有效克服了图像畸变和重复采集的问题。然后，引入自适应图像校正方法来校正曝光不足和曝光过度的图像。使用基于尺度不变特征变换（SIFT）特征图像注册的图像缝合方法来连接列车底部图像。最后，将所开发的监测系统应用于广州地铁 21 号线。结果表明，所开发的校正方法能有效校正曝光不足和曝光过度的图像。在确定重叠区域后进行特征匹配，减少了迭代次数，提高了系统的拼接速度。与现有方法相比，拼接后的图像在峰值信噪比（PSNR）、结构相似度（SSIM）和边缘图差值（DoEM）方面都具有更高的质量。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice

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