# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
###### Abstract  
Automated car damage detection using computer vision techniques has been studied using several datasets, but real cases for insurance companies are usually dependent on private methods and datasets. Furthermore, there are no metrics or standardized processes that describe the situation in which the company analyzes the customer's images, the models used for the inference, and the results. We perform extensive experiments to show that our proposal, an ensemble of 10 deep learning detectors based on YOLOv5, improves the state-of-the-art not only in terms of typical metrics but also in terms of inference speed, allowing scalability to thousands of instances per minute. A comparison with YOLOv8 is carried out, showing the differences between both ensembles. Furthermore, a dataset called TartesiaDS, labeled under the supervision of professional appraisers from insurance companies, is available to the community for evaluation of future proposals. object detection; car damage detection; car damage assessment; ensemble; supervised learning; deep learning +
Footnote †: journal: _Sci._  
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learning models require a sufficient amount of data to achieve acceptable performance, it is possible to turn to public datasets such as [PERSON] et al. 's work [4], where 4000 high-resolution car damage images with more than 9000 cases of six damage categories are provided. However, in order to perform vehicle evaluation and recognize damage as an expert would, it is necessary to have a dataset similar to that obtained by an appraiser, usually between six and eight images of the exterior of the vehicle, without occlusions and at an adequate distance. For this reason, in this work, we have focused on creating a dataset under the supervision of appraisers, which has 7896 images of vehicles with the presence of damage intended for the training process. Additionally, we contribute a public dataset of 108 images, again under the supervision of insurance appraisers, to allow for comparisons with future models. To the best of our knowledge, there is no public dataset of this type. Another important feature about both datasets is that the photo acquisition was guided by a web app-based process that ensures very uniform shooting angles and distances to the picture target throughout all the images. We should also take into account the requirements of insurance companies for a car damage detector:  
1. The ultimate goal of the industry is to make the damage assessment process require as little supervision as possible, so very high precision values (i.e., low false positive rate) are preferred. 2. In terms of efficiency, we need bounded execution times, as well as a system that is easily scalable and parallelizable and able to respond to dozens of concurrent requests. Based on our dataset, we propose an inference voting system based on single-stage YOLOv5 and YOLOv8-type detectors, applying ensemble learning and confidence threshold optimization. Our proposal succeeds in identifying vehicle damage types while maintaining the minimum number of false positives. Furthermore, we make a comparison with existing damage detection models and demonstrate a significant improvement in both performance metrics (Area under the ROC Curve (AUC), precision, and recall) and inference times, making our model suitable for scalable applications in insurance companies and reducing claims leakage. In summary, the main contributions of this study are the following:  
1. We propose an ensemble of YOLOv5 detectors and another of YOLOv8 detectors combined with confidence threshold optimization and new bounding box generation for the detection of car damage for insurance companies. 2. We compare our model with existing models and surpass the state-of-the-art in damage detection in both performance and inference time while keeping the number of false positives low. 3.