Track Intrusion Detection and Track Integrity Evaluation

Qian, Yu; Tang, Youzhi; Rizos, Dimitris C; Vitzilaios, Nikolaos

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Track Intrusion Detection and Track Integrity Evaluation

2024-09-30
By Qian, Yu ; Tang, Youzhi ; Rizos, Dimitris C

File Language:

English

Details

Creators:

Qian, Yu ; Tang, Youzhi ; Rizos, Dimitris C ; Vitzilaios, Nikolaos
Corporate Creators:

University of South Carolina. Molinaroli College of Engineering and Computing
Corporate Contributors:

University Transportation Center for Railway Safety (UTCRS) UTC ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

UTCRS-USC-O5CY23
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

June 1, 2023 – August 31, 2024
Corporate Publisher:

University Transportation Center for Railway Safety (UTCRS) Tier-1 University Transportation Center (UTC)
Abstract:

Track intrusion, particularly trespassing within railroad rights-of-way, poses a major safety risk, leading to more fatalities than train-vehicle collisions. This research introduces a Hybrid Region-based Convolutional Network (Hybrid-RCNN) that integrates foreground segmentation and object detection to enhance surveillance at rail crossings. The model performs multiple tasks, including object detection, classification, and tracking, to efficiently monitor unauthorized activities. Testing shows the Hybrid-RCNN's superior accuracy compared to models like Mask-RCNN, highlighting its potential to improve railway safety by identifying and mitigating hazards more effectively.
Format:

PDF
Funding:

69A3552348340
Download URL:

https://rosap.ntl.bts.gov/view/dot/79103/dot_79103_DS1.pdf
File Type:

[PDF - 955.10 KB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:de77758665013ce0437a8058869856a7b111d2d93afa02469624a1f13b2024e1171dd7a1a3e8ecd83caac7fd776827770f15840e4baf560f2b16a8d4d29ac726

File Language:

English

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