YOLO-MT-A-Lightweight-Multi-Task-Learning-Framework-

You can find the inference and training code from bdd100k.ipynb notebook.

• GitHub: https://github.com/kursatkomurcu/YOLO-MT-A-Lightweight-Multi-Task-Learning-Framework-

🚗 YOLO-MT: Lightweight Multi-Task Learning for Unified Scene Understanding

YOLO-MT is a YOLOv12-based lightweight multi-task learning framework that performs object detection, lane detection, drivable area segmentation, and scene attribute classification within a single unified model.
It is specifically designed for real-time autonomous driving and embedded systems with limited computational resources.

✨ Key Features

• ✅ 4 tasks in a single network
  • Object Detection
  • Lane Detection
  • Drivable Area Segmentation
  • Scene Attribute Classification (weather, scene type, time of day)
• ⚡ Real-time inference
• 📦 Only 2.9M parameters
• 🧠 Shared YOLOv12 backbone
• 🔥 Optimized for embedded and edge devices
• 📊 Trained and evaluated on the BDD100K dataset

🏗️ Architecture Overview

• Shared YOLOv12 encoder
• Lightweight multi-branch decoder
• ASPP-Lite module for context aggregation
• Separate task-specific heads for:
  • Lane segmentation
  • Drivable area segmentation
  • Attribute classification

🎯 Training Strategy

1. Train YOLOv12 on object detection
2. Freeze the backbone
3. Jointly train:
   • Lane detection
   • Drivable area segmentation
   • Attribute classification

This two-stage training ensures strong feature reuse while keeping the model extremely compact.

📁 Dataset

• BDD100K
  • Object detection annotations
  • Lane markings
  • Drivable area masks
  • Scene attributes (weather, scene type, time of day)

All images are resized to 384×640 for efficient real-time processing.

📌 Use Cases

• Autonomous driving perception
• Advanced Driver Assistance Systems (ADAS)
• Embedded AI systems
• Edge deployment for robotics

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Example outputs