Vision-Based Overload Detection System for Land Transportation

被引:0
|
作者
Wang, Yuntao [1 ,2 ]
Xia, Chengxi [2 ]
Sun, Haibo [2 ]
Zhang, Yihan [3 ]
Liu, Zheyan [3 ]
Wang, Yufei [4 ]
Xu, Naixuan [4 ]
Zhu, Jianjia [4 ]
Zhang, Yuchen [4 ]
Wu, Huaqiang [2 ,5 ]
Shi, Yuanchun [1 ,2 ]
机构
[1] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China
[2] Tsinghua Univ, Global Innovat Exchange Inst, Beijing, Peoples R China
[3] Beijing Univ Posts & Telecommun, Beijing, Peoples R China
[4] Trucker Beijing Technol Co Ltd, Beijing, Peoples R China
[5] Tsinghua Univ, Inst Microelect, Beijing, Peoples R China
关键词
Overload detection; Numeric digit recognition; Vehicle classification; Smart weighing station;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Overloaded trucks pose a threat to highway traffic, increasing the rate and severity of accidents, while damaging road infrastructure. Enabling an efficient overload detection method at existing weighing stations would facilitate regulatory compliance. This paper presents a real-time, accurate truck overload detection system that leverages existing surveillance cameras installed at weighing stations. To achieve this goal, we applied computer vision algorithms on video from an indoor camera monitoring a digital display and on another outdoor camera monitoring the station's weighing bridge. The truck's actual weight is obtained by reading the numeric digit display on images from the indoor camera, while the truck's maximum load capacity is estimated by recognizing the its wheel layout using the video from the outdoor camera. Through evaluation using video data from a weighing station, the optical digit recognition algorithm achieves an accuracy of 99.0%, while the load capacity estimation algorithm achieves an accuracy of 93.18%.
引用
收藏
页码:4915 / 4928
页数:14
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