YOLOv8 Vehicle Damage Detection Model

I'm seeking a computer vision or deep learning specialist to develop a YOLOv8-based object detection model. The goal is to accurately identify various types of damages on vehicles, such as scratches, dents, cracks, broken or missing parts (e.g., bumpers, mirrors, lights), and broken or damaged glasses. The model should be capable of detecting these damages on a range of vehicles, including cars, buses, trucks, and motorcycles.

Key Requirements:
- Develop a YOLOv8 object detection model for vehicle damage identification.
- The model must detect scratches, dents, cracks, broken/missing parts, and damaged glasses.
- Train the model to work with multiple vehicle types: cars, buses, trucks, and motorcycles.
- Since no data is currently available, assistance in sourcing or generating annotated training data is required.

Ideal Skills and Experience:
- Strong background in computer vision and deep learning.
- Experience with YOLO models, particularly YOLOv8.
- Proficiency in handling and annotating large datasets for model training.
- Ability to work with diverse vehicle types and damage categories.
- Problem-solving skills to address data acquisition and model training challenges.

If you have the expertise to build a robust damage detection model from scratch, I would love to hear from you. Your insights on data sourcing and model optimization will be highly valued.