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Artificial Intelligence-Based Surveillance System for Railway Crossing Traffic

机译：基于人工智能的铁路交通交通监测系统

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The application of Artificial Intelligence (AI) based techniques has strong potential to improve safety and efficiency in data-driven Intelligent Transportation Systems (ITS) as well as in the emerging Internet of Vehicles (IoV) services. This paper deals with the practical implementation of deep learning methods for increasing safety and security in a specific ITS scenario: railway crossings. This research work presents our proposed system called Artificial Intelligence-based Surveillance System for Railway Crossing Traffic (AISS4RCT) that is based on a combination of detection and classification methods focusing on various image processing inputs: vehicle presence, pedestrian presence, vehicle trajectory tracking, railway barriers at railway crossings, railway warnings, and light signaling systems. The designed system uses cameras that are suitably positioned to capture an entire crossing area at a given railway crossing. By employing GPU accelerated image processing techniques and deep neural networks, the system autonomously detects risky and dangerous situations at railway crossing in real-time. In addition, camera modules send data to a central server for further processing as well as notification to interested parties (police, emergency services, railway operators). Furthermore, the system architecture employs privacy-by-design and security-by-design best practices in order to secure all communication interfaces, protect personal data, and to increase personal privacy, i.e., pedestrians, drivers. Finally, we present field-based results of detection methods, and using the YOLO tiny model method we achieve average recall 89%. The results indicate that our system is efficient for evaluating the occurrence of objects and situations, and it's practicality for use in railway crossings.
机译：基于人工智能（AI）技术的应用具有强大的潜力，可以提高数据驱动智能运输系统（其）以及新出现的车辆（IOV）服务中的安全性和效率。本文涉及深入学习方法的实际实施，以提高其特定情况下的安全和安全性：铁路交叉路口。这项研究工作提出了我们所提出的系统，称为人工智能的监控系统，用于铁路交通（AISS4RCT），其基于专注于各种图像处理输入的检测和分类方法的组合：车辆存在，行人存在，车辆轨迹跟踪，铁路铁路交叉路口，铁路警告和光信号系统的障碍。设计的系统使用适当定位的相机，以在给定铁路交叉处捕获整个交叉区域。通过采用GPU加速的图像处理技术和深神经网络，系统在实时地自主检测铁路交叉的风险和危险情况。此外，相机模块将数据发送到中央服务器，以进一步处理以及有关各方（警方，紧急服务，铁路运营商）的通知。此外，系统架构采用虚拟设计和逐个设计的最佳实践，以确保所有通信接口，保护个人数据，并增加个人隐私，即行人，司机。最后，我们呈现了基于现场的检测方法结果，并使用yolo微型模型方法，我们达到平均召回89％。结果表明，我们的系统对于评估物体和情况的发生是有效的，并且在铁路交叉口使用的实用性。

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