🚀 AI Helmet Detection System

YOLOv8 • Roboflow Dataset • Real‑Time Tracking

       

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⚡ Overview

This project implements a custom-trained YOLOv8 model that detects whether motorcycle riders are wearing helmets. The model was trained for 100 epochs using a high‑quality Roboflow dataset and includes image detection, video detection, and real‑time object tracking with persistent IDs.

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🧠 Features

🔹 AI Helmet Detection

• Detects With Helmet / Without Helmet
• High accuracy with custom YOLOv8 weights
• Bounding boxes + confidence overlay

🔹 Image Detection

helmet_detection_image.py — Runs helmet detection on any image.

🔹 Video Detection + Tracking

helmet_detection_video.py supports:

• YOLOv8 tracking with persist=True
• Re‑identification of riders
• Smooth real-time predictions

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📊 Model Performance (Visual Results)

🔵 Precision–Confidence Curve

🔵 Recall–Confidence Curve

🔵 F1–Confidence Curve

🔵 Confusion Matrix

🔵 Precision–Recall (mAP@0.5)

🔵 Training Curves (YOLOv8)

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📈 Key Metrics (YOLOv8)

• mAP@0.5: 0.896
• Precision: Helmet – 0.936, No Helmet – 0.856
• Stable curves and strong detection accuracy for both classes

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⚙️ Installation

1️⃣ Install dependencies

pip install ultralytics cvzone opencv-python numpy

2️⃣ Run Image Detection

python helmet_detection_image.py

3️⃣ Run Video Detection

python helmet_detection_video.py

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🧬 Training Details

• Dataset: Roboflow (Bike Helmet Detection)
• Epochs: 100
• Framework: YOLOv8 (Ultralytics)
• Resolution: 640×640
• Optimizer: Default YOLO settings

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