用卷积神经网络自动检测停车位真值，人力成本降低99.58%。
Parking Space Ground Truth Test Automation by Artificial Intelligence Using Convolutional Neural Networks
- 用CNN自动识别车载超声传感器采集的停车数据
- 自动化测试使人工耗时减少99.58%
- 适合智慧交通与自动驾驶数据标注场景
本研究是基于众包车载车队数据的实时云端路边停车服务的一部分。该服务通过分类车载超声传感器采集的众包检测数据，提供停车位实时可用信息。本文旨在通过分析停车位真值测试流程的自动化，优化现有停车服务的质量。为此，应用机器学习领域中的图像模式识别技术，特别是卷积神经网络（CNN），以扩充数据库并替代分析过程中的主要人工工作。在介绍相关机器学习背景后，论文详细说明了实现高自动化程度的方法与实施过程。最后，通过预设指标评估性能，结果显示人工资源消耗减少达99.58%。整体改进效果被讨论、总结，并对未来发展方向及分析自动化工具的应用潜力进行了展望。
原文摘要 · Abstract (English)
This research is part of a study of a real-time, cloud-based on-street parking service using crowd-sourced in-vehicle fleet data. The service provides real-time information about available parking spots by classifying crowd-sourced detections observed via ultrasonic sensors. The goal of this research is to optimize the current parking service quality by analyzing the automation of the existing test process for ground truth tests. Therefore, methods from the field of machine learning, especially image pattern recognition, are applied to enrich the database and substitute human engineering work in major areas of the analysis process. After an introduction into the related areas of machine learning, this paper explains the methods and implementations made to achieve a high level of automation, applying convolutional neural networks. Finally, predefined metrics present the performance level achieved, showing a time reduction of human resources up to 99.58 %. The overall improvements are discussed, summarized, and followed by an outlook for future development and potential application of the analysis automation tool.
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