yolov4_tt100k

Introduction

The YOLO v4 Chinese Traffic Sign Recognition model is a real-time object detection system specifically optimized for China's complex traffic environments. Built on the YOLO algorithm, it incorporates efficient feature extraction networks, multi-scale prediction mechanisms, attention mechanisms, and improved spatial pyramid pooling techniques. These enhancements enable the model to accurately recognize traffic signs of various sizes and angles under different lighting and weather conditions. Tested on the CCTSDB 2021 dataset, it achieved a detection accuracy of 96.62%, a recall rate of 79.73%, an F-1 score of 87.37%, and an mAP of 92.77%, with a high frame rate of approximately 81 frames per second. This makes it suitable for real-time applications in intelligent transportation systems, autonomous vehicles, urban surveillance, and industrial automation.

Architecture

The model employs a deep learning architecture based on YOLO v4, optimized with advanced techniques such as attention mechanisms and spatial pyramid pooling. These improvements enhance its ability to detect and recognize traffic signs accurately across various challenging conditions.

Training

The model was trained on the CCTSDB 2021 dataset, focusing on optimizing detection accuracy and recall rates while maintaining a high frame rate. This ensures it meets the real-time requirements necessary for applications in intelligent transportation and autonomous systems.

Guide: Running Locally

1. Clone the Repository:

   git clone git@hf.co:Genius-Society/yolov4_tt100k
   cd yolov4_tt100k
   

2. Download the Model:

   from modelscope import snapshot_download
   model_dir = snapshot_download('Genius-Society/yolov4_tt100k')
   

3. Run the Model:

   • Use the downloaded model directory to test and deploy the model in your environment.

Cloud GPUs: Consider using cloud GPU services such as AWS EC2, Google Cloud Platform, or Azure for enhanced computation speed and efficiency.

License

This project is licensed under the MIT License, allowing for flexibility in usage and distribution.