Vehicle Parts and Damage Instance Segmentation Dataset with 29 Classes

Car Dent and Parts Detection Using YOLOv8 is a dataset for instance segmentation tasks. It contains images annotated for 29 classes related to vehicle parts and damage. The dataset is hosted on Kaggle and is formatted for use with the YOLOv8 object detection model.

Use Cases

• Training instance segmentation models to detect vehicle dents based on the described damage classes.
• Developing automated systems for vehicle part identification and localization.
• Creating tools for insurance claim processing by automatically segmenting damaged areas.
• Benchmarking the performance of YOLOv8 and similar architectures on a specialized automotive task.

Strengths

• Dataset is specifically designed for instance segmentation, a more detailed task than bounding-box detection.
• Annotations cover 29 distinct classes for vehicle parts and damage types.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Kaggle

Collection Method

Likely contains annotated images of vehicles, but the specific collection method is not detailed.