A RASPBERRY PI SELF-DRIVING CART BASED ON OPENCV AND DEEP LEARNING .

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Abstract

The self-driving trolley created in this thesis uses cameras and ultrasonic sensors to obtain roadway information, and a deep learning based target recognition algorithm to find out which are the targets in the data obtained, so that the trolley can drive itself on a simulated roadway with functions such as obstacle avoidance and traffic signal recognition. Originally the car used a Raspberry Pi 3b+, but here the jetson nano, which is better than the Raspberry Pi 3b+, is used to implement it.
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基于openv和深度学习的树莓派自动驾驶车。
本文制作的自动驾驶小车使用摄像头和超声波传感器获取道路信息,并使用基于深度学习的目标识别算法在获取的数据中找出哪些是目标,从而使小车在模拟的道路上自动行驶,具有避障和交通信号识别等功能。最初,这辆车使用的是树莓派3b+,但在这里,比树莓派3b+更好的jetson nano被用来实现它。
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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