Moving video-based detection for roadside illegally parking vehicles

doi:10.11947/j.AGCS.2025.20250038

Abstract

Abstract:

Roadside parking zones play a significant role in alleviating urban parking pressure. However, with the continuous growth in urban motor vehicle ownership, the supply-demand gap for roadside parking spaces continues to widen, leading to severe illegally parking that significantly impacts traffic efficiency and safety. Existing illegally parking monitoring systems based on fixed-point cameras or sensors suffer from high costs and limited coverage. To address this, this paper proposes a solution for detecting suspected illegally parked vehicles in roadside parking zones using mobile cameras. The solution is developed using embedded devices combined with an improved object detection algorithm (achieving a 3.3% increase in mAP@50). Trained on a custom-built dataset, it enables real-time detection of suspected illegally parked vehicles and effectively supports large-area monitoring tasks for suspected illegal parking. Comparative analysis with simultaneous drone-tracked orthophoto imagery from the same road sections showed that the solution achieves an average precision of 0.87, a recall of 0.88, and a detection speed of 53.96 frames per second (fps), fully validating the feasibility and effectiveness of the proposed method.

Key words: embedded devices, obeject detection, suspected illegally parked vehicles

CLC Number:

P237

Kang TANG, Yu SUN, Xiaoyang ZHONG, Jialiang GAO, Chongcheng CHEN. Moving video-based detection for roadside illegally parking vehicles[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(9): 1712-1726.

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虚拟线位置	总车辆数	触发数	触发比
0.6×width	61	58	0.95
0.7×width	61	60	0.98
0.8×width	61	34	0.56

Tire_1	Tire_2	Tire_3	Tire_4	疑似违停	合理停泊
前	后				√
前		后		√
	前&后			√
前			后	√
	前	后		√
	后	前		√
后	前			√
后			前	√
		后	前	√
			前&后	√

配置	Jetson Orin NX	PC
OS	Ubuntu 20.04 LTS	Windows 10
CPU	6-core NVIDIA Arm Cortex A78AE v8.2 64 bit CPU 2MB L2+4MB L3	Intel（R）Core（TM）i7-11700
GPU	1024-core NVIDIA Ampere GPU with 32 Tensor Cores	GTX1660Super
Memory	16 GB 128 bit LPDDR5 102.4 GB/S	32GB（2933 MHz/3200 MHz）

标签类别	标签数量	占比/（%）
Vehicle	1980	32
Tire_1	1267	20
Tire_2	1017	16
Tire_3	710	11
Tire_4	1280	20

边框损失函数	查准率	召回率	mAP@50
CIoU	0.911	0.908	0.941
MPDIoU	0.923	0.922	0.951
Wise-IoU	0.928	0.898	0.942
Inner-IoU	0.902	0.904	0.939