recepayddogdu/Object_Detection_Classification_-_Ford_Otosan_Intern_P2
Development of Deep Learning algorithms for Drivable Area Segmentation, Lane Segmentation, Traffic Sign Detection and Classification with data collected and labeled by Ford Otosan.
This project helps automotive engineers develop advanced driver-assistance systems by analyzing real-world driving footage. It takes video from vehicle cameras as input and identifies critical elements like lane markings and traffic signs, outputting detailed visual overlays. Automotive engineers and researchers in autonomous driving can use this to train and test self-driving car algorithms.
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Use this if you need to automatically detect and classify traffic signs and identify lane boundaries in highway driving videos to enhance autonomous vehicle perception systems.
Not ideal if your primary need is for general object detection unrelated to road conditions or if you require real-time processing on embedded systems without significant computational resources.
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Feb 18, 2022
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