# YOLOv5至YOLOv12升级-交通标志识别系统

**Repository Path**: chw91/TrafficSignRecognition

## Basic Information

- **Project Name**: YOLOv5至YOLOv12升级-交通标志识别系统
- **Description**: 交通标志识别系统，专注于利用AI技术实现高效准确的交通标志识别，支持多种环境和条件下的实时识别，适用于智能驾驶、交通监控等场景。
- **Primary Language**: Python
- **License**: AGPL-3.0
- **Default Branch**: master
- **Homepage**: https://www.bilibili.com/video/BV1gNpXzgEgh/
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2026-04-13
- **Last Updated**: 2026-04-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Traffic Sign Recognition System Based on the YOLO Series

![License](https://img.shields.io/badge/License-AGPL--3.0-brightgreen)
![Python](https://img.shields.io/badge/Python-3.10%2B-blue)
![PyTorch](https://img.shields.io/badge/PyTorch-2.2%2B-orange)

## Project Overview

Traffic sign recognition plays a crucial role in intelligent driving and road safety. This project builds a traffic sign recognition system based on YOLOv5 through YOLOv12, supporting real-time detection and classification across multiple categories such as speed limits, prohibitions, and warnings.

The desktop UI is developed with PySide6 and supports images, videos, and camera input. Built-in features include heatmap visualization, class statistics, Conf/IoU parameter tuning, and real-time result rendering. User management and result persistence are implemented with an SQLite database. The project provides complete code and datasets, along with a systematic comparison of mAP, F1, PR metrics and training curves across YOLO versions to facilitate model reproduction and real-world engineering deployment.

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## Resources

* **YOLOv5–12 (8-in-1) bundle download:** [https://mbd.pub/o/bread/mbd-YZWXmZ1uaA==](https://mbd.pub/o/bread/mbd-YZWXmZ1uaA==)
* **Complete resources for YOLOv12:** [https://mbd.pub/o/bread/mbd-YZWXmJhrZg==](https://mbd.pub/o/bread/mbd-YZWXmJhrZg==)
* **Complete resources for YOLOv11:** [https://mbd.pub/o/bread/mbd-YZWXmZ1sZw==](https://mbd.pub/o/bread/mbd-YZWXmZ1sZw==)
* **Complete resources for YOLOv10:** [https://mbd.pub/o/bread/mbd-YZWXmZ1sbA==](https://mbd.pub/o/bread/mbd-YZWXmZ1sbA==)
* **Complete resources for YOLOv9:** [https://mbd.pub/o/bread/mbd-YZWXmZ1tZw==](https://mbd.pub/o/bread/mbd-YZWXmZ1tZw==)

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## Environment Setup & Run Guide

We recommend using PyCharm + Miniconda + Python for environment setup and project execution. Detailed tutorials:

1. **PyCharm installation:** [Watch video](https://www.bilibili.com/video/BV1yypczDEP6/)
2. **Miniconda installation:** [Watch video](https://www.bilibili.com/video/BV1f5pcz3EgS/)
3. **Python environment configuration:** [Watch video](https://www.bilibili.com/video/BV1F3pcz5EgY/)

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## Project Features

* Supports multiple model versions from YOLOv5 to YOLOv12
* Real-time object detection and classification for images, videos, and live camera feeds
* Desktop visualization UI implemented with PySide6
* Heatmaps, class statistics, and parameter tuning
* SQLite database for user management and data persistence
* Systematic model performance comparisons (mAP, F1, PR, training curves)

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## Use Cases

Suitable for intelligent driving, autonomous driving R\&D, traffic safety monitoring, and related AI vision projects.

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For more details, please refer to the resource and tutorial links above. Welcome to download and try it out!