The project "YOLO_v8_Road_Sign_Detection" is a computer vision project that aims to detect road signs using a deep learning model based on the YOLO (You Only Look Once) algorithm. The repository contains the source code and trained models for the YOLO_v8_Road_Sign_Detection project. The project is implemented using Python and TensorFlow.
The code consists of several modules, including data processing, model training, and testing. The data processing module includes scripts to preprocess the input images and convert them into a format suitable for training the YOLO_v8 model. The model training module includes scripts to train the YOLO_v8 model using a labeled dataset of road signs. The testing module includes scripts to evaluate the performance of the trained model on a test dataset.
The repository also includes pre-trained models that can be used for road sign detection without training the model from scratch. The models are provided in several formats, including TensorFlow SavedModel, TensorFlow Lite, and OpenCV.
The project also includes a demo script that can be used to test the performance of the model on a live video stream or a pre-recorded video file. The script detects road signs in real-time and displays the results on the screen.
Overall, the "YOLO_v8_Road_Sign_Detection" project is a useful tool for detecting road signs in images and videos, which can be helpful for various applications such as autonomous driving, traffic analysis, and road safety.