Minor Dent Detection Dataset: High-Quality Images for Automotive Damage Detection

This is the initial public release of the Minor Dents Detection Dataset curated for the detection of small-scale dents on metallic car surfaces using deep learning techniques. 🔍 Overview: The dataset consists of 2241 high-quality images collected manually from real-world environments such as workshops, roadsides, and urban areas. The primary focus is on hail-induced minor dents which are difficult to detect under normal conditions. 📁 Contents: Train Images: 1568 Validation Images: 449 Test Images: 224 Format: YOLOv8-compatible (images and corresponding annotations) 🛠 Key Highlights: Captured in diverse lighting conditions Includes preprocessing for dent visibility (contrast enhancement, shadow normalization) Focuses specifically on small, subtle dents often ignored by other datasets Suitable for use in automotive damage detection models 📘 Use Cases: This dataset is ideal for training and validating object detection models such as YOLOv8, SSD, and Faster R-CNN for the purpose of automated dent detection in car inspection workflows. 📄 License: CC BY 4.0 (Creative Commons Attribution 4.0) 📌 Citation & DOI: DOI will be generated via Zenodo and added here once available. Maintained by: Muhammad Danish Zia Baig