AnshChoudhary/Lane-Detection-UNet
This repository contains the implementation of a lane detection system using the UNet architecture. The model is trained on the BDD100K dataset, leveraging its diverse and large-scale data to ensure robust performance under various weather conditions and different times of day.
This project helps self-driving car engineers and researchers integrate accurate lane detection into their autonomous driving systems. It takes raw video or image feeds from vehicle cameras and outputs video with segmented lane markings, allowing for precise environmental understanding. This tool is designed for autonomous vehicle developers and researchers focused on improving perception systems.
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Use this if you need to identify and segment lane markings from diverse real-world driving footage to enhance autonomous vehicle navigation and perception.
Not ideal if you are looking for a plug-and-play solution for a commercial product without a development team, or if your primary need is object detection rather than lane segmentation.
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19
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Language
Python
License
MIT
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Last pushed
Jul 23, 2024
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