🚗 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
Train YOLOv12 on object detection
Freeze the backbone
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.
