Fueling Vehicle Visual Inspection Models with Damage Image Data Collection

Client's Challenge & Our Solution

A leading AI company developing vehicle inspection models required a comprehensive dataset of vehicle images showcasing various types of damage for training their visual inspection system. The client needed diverse images of vehicle damage from a wide range of countries and environments to improve their model’s ability to detect and classify damage accurately.

FutureBeeAI was tasked with collecting over 30,000 images of vehicle damage from regions including India, USA, Germany, Spain, UK, Australia, Hong Kong, and the Philippines. Our global crowd community enabled us to gather a wide variety of real-world vehicle damage images within just 4-5 weeks, ensuring the dataset was diverse, high-quality, and representative of different vehicle types and damage scenarios.

Outcome & Features:

Collected 30,000+ images of vehicle damage across multiple countries, representing various types of damage (e.g., dents, scratches, cracks, etc.).

Provided a diverse dataset featuring multiple regions to ensure the model can adapt to various environmental factors and vehicle conditions.

Delivered the dataset within 4-5 weeks, ensuring fast turnaround and high-quality images to train the client’s vehicle inspection system for greater accuracy.