Deep Learning Based Pothole Detection

D. Rajan, Mohammad Khaja Faizan, Rajinikanth Kundelu, Neha Nandal, Vasu Sena Gunda
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Abstract

Potholes are formed due to wear and tear and weathering of roads. They cause not only discomfort to citizens but also deaths due to vehicle accidents. In many developing countries and developed countries, the main problem is road deterioration. In the US nearly 2000 accidents were recorded due to potholes and road damage. The main aim of this project is to decrease the road accidents happening daily around the world. Many people while driving a car cannot see some of the potholes on the road and if they do not slow down their vehicles, there is a chance of an accident or vehicle damage. Therefore to decrease this kind of accident we came up with a project which recognizes potholes on the roads and alerts the driver by making a beep sound. To accomplish this objective we used the Yolo algorithm for pothole detection which uses neural networks. If the cameras can be installed on moving vehicles, then the potholes can be detected in real-time and avoided by alerting the driver.
基于深度学习的坑洞检测
坑洼是由于道路的磨损和风化而形成的。它们不仅会给市民带来不适,还会造成交通事故的死亡。在许多发展中国家和发达国家,主要问题是道路恶化。在美国,有近2000起事故是由于坑洼和道路损坏造成的。这个项目的主要目的是减少世界各地每天发生的交通事故。许多人在开车时看不到道路上的一些坑洼,如果他们不放慢车速,就有可能发生事故或车辆损坏。因此,为了减少这类事故,我们想出了一个项目,它可以识别道路上的坑洼,并通过发出哔哔声来提醒司机。为了实现这一目标,我们使用Yolo算法进行坑检测,该算法使用神经网络。如果摄像头可以安装在行驶中的车辆上,那么就可以实时检测到坑洼,并通过提醒司机来避免坑洼。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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