MCF-Defect (Synthetic Samples): Augmented Glossy Motorcycle Fairing Defect Dataset

This dataset contains a representative sample of synthetic images generated for our research on real-time defect detection for glossy motorcycle fairings. Important Note on Data Availability: > Due to a strict commercial Non-Disclosure Agreement (NDA) with the manufacturing partner, the original high-resolution raw images and proprietary part designs cannot be made publicly available. To ensure transparency and demonstrate the efficacy of our proposed data augmentation pipeline, we provide these synthetic macro-level samples. The dataset showcases the visual characteristics of 6 defect classes (Scratch, Dent, Bubble, Foreign Material, Gloss Loss, and Pinhole), specifically highlighting how our diffusion-based approach realistically preserves specular reflection contexts on highly reflective surfaces.