Towards Safer Highway Work Zones: Insights from Deep Learning Analysis of Thermal Footage

被引:0
|
作者
Bhuyan, Zubin [1 ]
Xie, Yuanchang [2 ]
Liu, Ruifeng [1 ]
Cao, Yu [1 ]
Liu, Benyuan [1 ]
机构
[1] Univ Massachusetts Lowell, Miner Sch Comp & Informat Sci, 1 Univ Ave, Lowell, MA 01854 USA
[2] Univ Massachusetts Lowell, Dept Civil Environm Engn, 1 Univ Ave, Lowell, MA 01854 USA
来源
IFAC PAPERSONLINE | 2024年 / 58卷 / 10期
关键词
Highway work zone; merge control; thermal video; vehicle tracking; safety;
D O I
10.1016/j.ifacol.2024.07.338
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Thermal imaging, when coupled with deep-learning-based analysis, can significantly improve highway work zone management and safety, though data scarcity in this field has been a historical challenge. We collected over 440 hours of thermal footage from highway sites in Massachusetts and New Hampshire and created a vehicle segmentation dataset featuring over 14,000 vehicle instances. We implement effective training strategies for deep learning models and develop algorithms to analyze vehicle trajectories and merging behaviors. Various visualization techniques are illustrated to better understand the extensive analytics data generated for each site, which can lead to development of data-driven strategies to improve traffic management and safety. GitHub repository: https://github.com/z00bean/SmartWorkZoneControl. Copyright (c) 2024 The Authors.
引用
收藏
页码:188 / 193
页数:6
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