基于人工神经网络的恶劣天气条件下目标检测系统

Jeberson A Joshua, L. Narasiman, V. Yogeshwaran, M. Anand
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引用次数: 0

摘要

用于汽车供应链和运输的车辆必须能够在各种天气和能见度下运行。雾、雪、光、烟、雨等的突然变化对供应链来说是具有挑战性的条件。虽然普通的雷达和摄像头在大多数驾驶情况下都能正常工作,但在一些边缘情况和更容易发生事故的情况下,车辆可能会出现故障。很多车祸发生在山区和高海拔地区,因为大雾、烟雾甚至烟雾使人们看不清道路。因此,建议的系统可以用来解决这个问题。当人、动物或车辆以危险的方式进入该系统时,可能会导致事故,因此它可以在事故发生之前提供事故预防警告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object Detection System in Adverse Weather Conditions Using Ann
Vehicles used in the automotive supply chain and for transportation must be able to operate in all types of weather and visibility. Sudden variations in fog, snow, lighting, smoke, rain, etc. are challenging conditions for the supply chain. While normal radars and cameras will work fine in most driving situations, vehicles may malfunction in some edge cases and in situations where accidents are more likely to occur. Numerous car accidents happen in mountainous areas and on ghats because it is unable to see the road well due to strong fog, smoke, and even smog. So, the suggested system can be used to solve this issue. When a person, animal, or vehicle enters the proposed system in a risky manner, it may result in an accident, hence it offers accident prevention before it happens a warning.
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