Udacity Self-Driving Car Engineer Nanodegree projects.
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Updated
Mar 28, 2023 - C++
Udacity Self-Driving Car Engineer Nanodegree projects.
Traffic signs detection and classification in real time
capsule networks that achieves outstanding performance on the German traffic sign dataset
Traffic Sign Recognition - Fine tuning VGG16 + GTSRB
Implementation of darkflow on traffic sign detection and classification
A Flask WebApp which can predict the Traffic Signs🚦 using Deep Learning
Türkiye Trafik İşaretleri Veriseti - Turkish Traffic Sign Dataset
This project is part of the CS course 'Systems Engineering Meets Life Sciences I' at Goethe University Frankfurt. In this Computer Vision project, we present our first attempt at tackling the problem of traffic sign recognition using a systems engineering approach.
Perception algorithms for Self-driving car; Lane Line Finding, Vehicle Detection, Traffic Sign Classification algorithm.
Street Sign recognition using Tensorflows ObjectDetector
A Deep Neural Network to do traffic sign recognition
Computer Vision and Machine Learning related projects of Udacity's Self-driving Car Nanodegree Program
🖍️ This project achieves some functions of image identification for Self-Driving Cars. First, use yolov5 for object detection. Second, image classification for traffic light and traffic sign. Furthermore, the GUI of this project makes it more user-friendly for users to realize the image identification for Self-Driving Cars.
Code for the paper entitled "Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods".
In this project, a traffic sign recognition system, divided into two parts, is presented. The first part is based on classical image processing techniques, for traffic signs extraction out of a video, whereas the second part is based on machine learning, more explicitly, convolutional neural networks, for image labeling.
Traffic sign recognition with Deep Convolutional Neural Networks
Application which detects traffic signs using camera
Using synthetic data in combination with Deep Learning, to determine if a system can be made that will be able to recognise and classify correctly real traffic signs.
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