VehiDE Dataset: New dataset for Automatic vehicle damage detection in Car insurance
2023 15th International Conference on Knowledge and Systems Engineering (KSE)(2023)
Abstract
In the world of auto insurance, automatic car damage identification has garnered a lot of interest. However, it is difficult for us to develop a workable model for car damage identification due to the absence of high-quality datasets that are accessible to the general public. In order to achieve this, the Vehicle Damage Detection (VehiDE) dataset, the large-scale dataset made available to the public for the purpose of segmenting and detecting visual automotive damage. This dataset comprises 13,945 high-resolution photos of damaged cars together with more than 32,000 occurrences of each damage category with detailed annotations. Statistical dataset analysis is provided together with a description of the image collecting, selection, and annotation procedures. In order to emphasize the expertise of automotive damage identification, extensive experiments on the VehiDE dataset are conducted using cutting-edge deep approaches for a variety of jobs and provide thorough analysis.
MoreTranslated text
Key words
Vehicle damage,Deep Learning,Mask-RCNN,Instances Segmentation,Insurance,Damage assessment
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined