UAV imagery based potential safety hazard evaluation for high-speed railroad using Real-time instance segmentation |
| |
| Affiliation: | 1. School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;2. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;3. Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC, USA;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. State Key Laboratory of Material Processing and Die & Mould Technology, Huazhong University of Science and Technology, Wuhan 430074, China;1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China;2. Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration (Beijing Institute of Technology), Ministry of Industry and Information Technology, Beijing 100081, China;3. Space Star Technology Co., Ltd, Beijing 100086, China;1. Department of Industrial Engineering & Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. Department of Transportation Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;3. School of Economics and Management, Beihang University, Beijing 100191, China |
| |
| Abstract: | Potential safety hazards (PSHs) along the track needs to be inspected and evaluated regularly to ensure a safe environment for high-speed railroad operations. Other than track inspection, evaluating potential safety hazards in the nearby areas often requires inspectors to patrol along the track and visually identify potential threads to the train operation. The current visual inspection approach is very time-consuming and may raise safety concerns for the inspectors, especially in remote areas. Using the unmanned aerial vehicle (UAV) has great potential to complement the visual inspection by providing a better view from the top and ease the safety concerns in many cases. This study develops an automatic PSH detection framework named YOLARC (You Only Look at Railroad Coefficients) using UAV imagery for high-speed railroad monitoring. First, YOLARC is equipped with a new backbone having multiple available receptive fields to strengthen the multi-scale representation capability at a granular level and enrich the semantic information in the feature space. Then, the system integrates the abundant semantic features at different high-level layers by a light weighted feature pyramid network (FPN) with multi-scale pyramidal architecture and a Protonet with residual structure to precisely predict the track areas and PSHs. A hazard level evaluation (HLE) method, which calculates the distance between identified PSH and the track, is also developed and integrated for quantifying the hazard level. Experiments conducted on the UAV imagery of high-speed railroad dataset show the proposed system can quickly and effectively turn UAV images into useful information with a high detection rate and processing speed. |
| |
| Keywords: | High-speed railroad Potential safety hazards (PSHs) UAV imagery Real-time instance segmentation Image processing |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
