Abstract—Future cars are anticipated to be driverless; point-to-point transportation services capable of avoiding fatalities. To achieve this goal, auto-manufacturers have been investing to realize the potential autonomous driving. In this regard, we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering, on a track made of colored cones. This paper goes through the process of fabricating a model vehicle, from its embedded hardware platform, to the end-to-end ML pipeline necessary for automated data acquisition and model-training, thereby allowing a Deep Learning model to derive input from the hardware platform to control the car’s movements. This guides the car autonomously and adapts well to real-time tracks without manual feature-extraction. This paper presents a Computer Vision model that learns from video data and involves Image Processing, Augmentation, Behavioral Cloning and a Convolutional Neural Network model. The Darknet architecture is used to detect objects through a video segment and convert it into a 3D navigable path. Finally, the paper touches upon the conclusion, results and scope of future improvement in the technique used. Keywords—autonomous, self-driving, computer vision, YOLO, object detection, embedded hardware
Design and Development of an Autonomous Car using Object Detection with YOLOv4
2022-01-15
oai:zenodo.org:5854560
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
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