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Combining deep learning methods and rule-based systems for automatic parking space detection
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DOI：10.3233/ICA-240745.png)
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摘要
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This paper presents an Automatic Parking Space Detection (APSD) algorithm designed to reduce traffic in cities while offering an information system of available parking zones. The main aim of such a system lies in its ability to identify parking spaces in a distributed manner, achieved by installing multiple APSD systems across a fleet of vehicles. This fleet, during its regular operations, communicates the availability of parking spaces to a centralized information system. Our methodology employs a rule-based system that seamlessly integrates a variety of neural networks for different specific tasks. These tasks include depth estimation, road segmentation, and vehicle detection. This approach would fall into a modular category instead of an end-to-end solution, using the M & aacute;laga Urban Dataset in the experiments. We present a preliminary experiment for parameter settings and an ablation study to quantify each subsystem contribution to the results. The proposed system achieves a parking space detection F1 score of 0.726.
Keyword:
Smart parking
parking space detection
detection networks
rule-based systems
automatic parking space detection
期刊
I
IF:
5.3
论文数:
487
被引数:
735
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引用论文
Vision-Based Parking-Slot Detection: A DCNN-Based Approach and a Large-Scale Benchmark Dataset基于视觉的停车位检测: 基于DCNN的方法和大规模基准数据集
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PREAI
分享收藏End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information集成全局和局部信息的端到端可训练单阶段停车位检测
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