Automatic image positioning of a rail train number using speed-up robust features and stroke width transform

Xing, Zongyi; Zhang, Zhen Yu; Yao, Xiaowen

doi:10.1177/09544062221095687

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…However, the network model is not end-to-end; thus, it is difficult to implement it. Z. Xing et al [16] addressed the problem of low contrast between train numbers and train bodies caused by insufficient lighting in tunnels and indoor environments. They used an improved singlescale Retinex algorithm based on brightness control for image enhancement.…”

Section: Introductionmentioning

confidence: 99%

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5

Duan

Qiu

Jin

et al. 2023

Sensors

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In order to meet the fast and accurate automatic detection requirements of equipment maintenance in railway tunnels in the era of high-speed railways, as well as adapting to the high dynamic, low-illumination imaging environment formed by strong light at the tunnel exit, we propose an automatic inspection solution based on panoramic imaging and object recognition with deep learning. We installed a hyperboloid catadioptric panoramic imaging system on an inspection vehicle to obtain a large field of view as well as to shield the high dynamic phenomena at the tunnel exit, and proposed a YOLOv5-CCFE object detection model based on railway equipment recognition. The experimental results show that the mAP@0.5 value of the YOLOv5-CCFE model reaches 98.6%, and mAP@0.5:0.95 reaches 68.9%. The FPS value is 158, which can meet the automatic inspection requirements of railway tunnel equipment along the line and has high practical application value.

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Section: Introductionmentioning

confidence: 99%

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5

Duan

Qiu

Jin

et al. 2023

Sensors

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“…Riaz et al [9] developed the MULTIMOORA method based on the q-ROF Einstein aggregation operator for MCDM to improve energy efficiency in lowincome households at the national level in Pakistan. Xing et al [10] proposed a fusion algorithm based The electronic water pump shell on SURF and SWT for regional railcar positioning to locate the enhanced images produced by the improved single-scale Retinex algorithm. The results show that the accuracy of the method is 96.91% in complex cases.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

Wu¹,

Wang²,

Fang³

et al. 2023

Computer Systems Science and Engineering

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For surface defects in electronic water pump shells, the manual detection efficiency is low, prone to misdetection and leak detection, and encounters problems, such as uncertainty. To improve the speed and accuracy of surface defect detection, a lightweight detection method based on an improved YOLOv5s method is proposed to replace the traditional manual detection methods. In this method, the MobileNetV3 module replaces the backbone network of YOLOv5s, depth-separable convolution is introduced, the parameters and calculations are reduced, and CIoU_Loss is used as the loss function of the boundary box regression to improve its detection accuracy. A dataset of electronic pump shell defects is established, and the performance of the improved method is evaluated by comparing it with that of the original method. The results show that the parameters and FLOPs are reduced by 49.83% and 61.59%, respectively, compared with the original YOLOv5s model, and the detection accuracy is improved by 1.74%, which is an indication of the superiority of the improved method. To further verify the universality of the improved method, it is compared with the results using the original method on the PASCALVOC2007 dataset, which verifies that it yields better performance. In summary, the improved lightweight method can be used for the real-time detection of electronic water pump shell defects.

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Rail wheel tread defect detection using improved YOLOv3

et al. 2022

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Automatic image positioning of a rail train number using speed-up robust features and stroke width transform

Cited by 4 publications

References 36 publications

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5

A Lightweight Electronic Water Pump Shell Defect Detection Method Based on Improved YOLOv5s

Rail wheel tread defect detection using improved YOLOv3

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