A Full-Scene Detection Algorithm for Underground Parking Spaces Fusing Vision and Geometric Reasoning

LIU Ping; MA Chunlei; LIU Mingjie; PIAO Changhao; HUANG Longhang; YAN Lupeng

doi:10.12263/DZXB.20260063

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A Full-Scene Detection Algorithm for Underground Parking Spaces Fusing Vision and Geometric Reasoning

PAPERS | 更新时间：2026-07-30

- A Full-Scene Detection Algorithm for Underground Parking Spaces Fusing Vision and Geometric Reasoning
- ACTA ELECTRONICA SINICA Vol. 54, Issue 4, Pages: 1692-1704(2026)
- 作者机构：
  
  1.重庆邮电大学自动化学院，重庆 400065
  2.重庆市建设信息中心，重庆 400010
- 作者简介：
- 基金信息：
  
  National key Research and Development program(2022YFE0101000);Chongqing Construction Information Center Project(JSXXCQ-CY01)
- DOI：10.12263/DZXB.20260063
  CLC： TP391.41;U495
- Received：11 March 2026，
  
  Accepted：25 March 2026，
  
  Online First：06 May 2026，
  
  Published：25 April 2026
- 稿件说明：
移动端阅览
刘平, 马春蕾, 刘明杰, 等. 融合视觉与几何推理的地下车位全场景检测算法[J]. 电子学报, 2026, 54(04): 1692-1704.

LIU Ping, MA Chunlei, LIU Mingjie, et al. A Full-Scene Detection Algorithm for Underground Parking Spaces Fusing Vision and Geometric Reasoning[J]. Acta Electronica Sinica, 2026, 54(04): 1692-1704.
刘平, 马春蕾, 刘明杰, 等. 融合视觉与几何推理的地下车位全场景检测算法[J]. 电子学报, 2026, 54(04): 1692-1704. DOI：10.12263/DZXB.20260063

LIU Ping, MA Chunlei, LIU Mingjie, et al. A Full-Scene Detection Algorithm for Underground Parking Spaces Fusing Vision and Geometric Reasoning[J]. Acta Electronica Sinica, 2026, 54(04): 1692-1704. DOI：10.12263/DZXB.20260063

摘要

在智能停车管理系统中，复杂环境下高精度车位感知是实现自动化引导与资源优化的核心技术。传统检测方法成本高、维护困难且车位状态信息单一，而基于视觉的方法虽能提供丰富信息，但在地下停车场面临光照昏暗、强光源干扰、背景复杂、车辆遮挡严重等挑战。为此，本文提出一种融合视觉与几何推理的轻量化地下停车场车位实时检测网络（Real-Time Underground Parking Space Occupancy DEtection TRansformer， RT-UPSO-DETR）。首先，设计了膨胀空间注意力轻量级残差F-DBlock（Fusion DBlock）模块，在扩大感受野捕获全局模糊特征的同时显著减少冗余计算。其次，构建了多机制融合的Transformer编码层CSAL（Cognitive Spatial Attention Layer）模块，替换RT-DETR-R18中原有的AIFI模块，显著提升了模型在低光照、模糊图像中的特征判别能力。进一步，针对车辆相互遮挡导致的后排车位漏检问题，设计了几何推理透视拓扑补全模块（Perspective Topology Completion Module， PTCM），利用前排已检测车位作为几何锚点估计透视灭点，基于径向投影推断被遮挡车位的近似位置，有效恢复缺失车位框。为验证算法有效性，本文构建了地下停车场车位检测数据集以及遮挡补全专项测试集，并在公开低光照数据集ExDark上开展泛化实验。实验结果表明，RT-UPSO-DETR在自建数据集上相较于基线RT-DETR-R18模型，在精度、召回率、mAP@0.5和mAP@0.5-0.95指标上分别提升1.5%、3.3%、0.7%和0.9%；同时参数量降低22.1%，GFLOPs降低18.8%，推理速度为52.4 FPS，满足实时检测需求。在ExDark数据集泛化实验中，本文模型较RT-DETR-R18在精确率、召回率、mAP@0.5和mAP@0.5-0.95上分别提升2.1%、1.3%、2.1%和1.0%，验证了模型在低光照条件下的鲁棒性。车位补全方面，PTCM模块在遮挡补全实验中在漏检召回率、补全准确率和平均中心点误差指标上分别达到了82.48%、74.83%和23.27 pixels，证明其能有效降低漏检率。本文提出的RT-UPSO-DETR网络通过轻量化设计与多机制注意力融合，有效解决了地下停车场低光照、复杂背景和遮挡场景下的车位检测难题，透视拓扑补全模块进一步增强了系统对遮挡的鲁棒性，为实际智能停车系统提供了可行的视觉解决方案。

Abstract

In intelligent parking management systems

achieving high-precision parking space perception in complex environments is a core technology for automated guidance and resource optimization. Traditional detection methods suffer from high costs

difficult maintenance

and limited parking status information. Although vision-based approaches can provide rich information

they face challenges in underground parking lots

such as dim illumination

strong light interference

complex backgrounds

and severe vehicle occlusion. To address these issues

this paper proposes a lightweight real-time underground parking space occupancy detection network integrating vision and geometric reasoning

termed real-time underground parking space occupancy detection transformer (RT-UPSO-DETR). First

a lightweight residual F-DBlock (Fusion DBlock) module with dilated spatial attention is designed

which expands the receptive field to capture global blurred features while significantly reducing redundant computation. Second

a multi-mechanism Transformer encoder layer

cognitive spatial attention layer (CSAL)

is constructed to replace the original AIFI module in RT-DETR-R18

significantly enhancing the model’s feature discrimination ability in low-light and blurred images. Furthermore

to address the missed detection of rear parking spaces caused by mutual vehicle occlusion

a geometric reasoning perspective topology completion module (PTCM) is devised

which uses the detected front parking spaces as geometric anchors to estimate the vanishing point and then infers the approximate positions of occluded spaces via radial projection

effectively recovering missing bounding boxes. To validate the effectiveness of the proposed algorithm

we construct an underground parking space detection dataset and a dedicated occlusion completion test set

and conduct generalization experiments on the public low-light dataset ExDark. Experimental results on the self-built dataset show that compared with the baseline RT-DETR-R18

RT-UPSO-DETR improves precision

recall

mAP@0.5

and mAP@0.5-0.95 by 1.5%

3.3%

0.7%

and 0.9%

respectively. Meanwhile

the number of parameters is reduced by 22.1%

GFLOPs by 18.8%

and the inference speed reaches 52.4 FPS

satisfying real-time detection requirements. On the ExDark dataset

compared with RT-DETR-R18

the proposed model improves precision

recall

mAP@0.5

and mAP@0.5-0.95 by 2.1%

1.3%

2.1%

and 1.0%

respectively

verifying its robustness under low-light conditions. For parking space completion

the PTCM achieves a missed recall rate of 82.48%

a completion accuracy of 74.83%

and a mean center error of 23.27 pixels

demonstrating its effectiveness in reducing missed detections. The proposed RT-UPSO-DETR network

through lightweight design and multi-mechanism attention fusion

effectively addresses the challenges of parking space detection in underground parking lots with low illumination

complex backgrounds

and occlusions. The perspective topology completion module further enhances the system’s robustness to occlusions

providing a feasible vision-based solution for practical intelligent parking systems.

关键词

Keywords

references

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