论文标题
自动化级别越过系统：带有覆盆子PI微控制器的基于计算机视觉的方法
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller
论文作者
论文摘要
在像孟加拉国这样迅速蓬勃发展的国家中，无人级别的交叉路口每天都在增加。这项研究提出了一种基于深度学习的方法，用于自动化级别的交叉连接，以确保最大的安全性。在这里，我们使用在微控制器上使用计算机愿景来开发一种完全自动化的技术，该技术将减少并消除级别的死亡和事故。 Raspberry Pi微控制器在实时视频上使用计算机视觉检测即将来临的火车，并且交叉路口已关闭，直到传入的火车未受阻碍。实时视频活动识别和对象检测算法扫描连接点24/7。自我调节的微控制器控制整个过程。当确定持续的未经授权活动时，通过自动消息和通知通知警察和消防队等当局。微控制器评估了实时铁路轨道数据，到达和出发时间，以预测ETA，火车位置，速度和轨道问题，以避免进行正面碰撞。该建议的计划以比当前市场解决方案低的成本减少了道路交叉事故和死亡。 索引术语：深度学习，微控制器，对象检测，铁路交叉，Raspberry Pi
In a rapidly flourishing country like Bangladesh, accidents in unmanned level crossings are increasing daily. This study presents a deep learning-based approach for automating level crossing junctions, ensuring maximum safety. Here, we develop a fully automated technique using computer vision on a microcontroller that will reduce and eliminate level-crossing deaths and accidents. A Raspberry Pi microcontroller detects impending trains using computer vision on live video, and the intersection is closed until the incoming train passes unimpeded. Live video activity recognition and object detection algorithms scan the junction 24/7. Self-regulating microcontrollers control the entire process. When persistent unauthorized activity is identified, authorities, such as police and fire brigade, are notified via automated messages and notifications. The microcontroller evaluates live rail-track data, and arrival and departure times to anticipate ETAs, train position, velocity, and track problems to avoid head-on collisions. This proposed scheme reduces level crossing accidents and fatalities at a lower cost than current market solutions. Index Terms: Deep Learning, Microcontroller, Object Detection, Railway Crossing, Raspberry Pi
