# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 6 Conclusions and Future Work  
In this paper, we have presented our implementation of a voting system and a box fusion process using ensemble learning to perform damage detection in cars. This is done in the context of insurance companies, where it is required to have a low false positive rate and a good efficiency for scalability.  
Through several tests, we managed to highlight the advantages of using ensemble learning based on YOLOv5 detectors, together with optimization and box fusion processes, allowing us to implement a fast and robust system and minimize the number of false positives.  
We also published a careful selection of 108 images containing vehicles with and without damage, named TartesiaDS. This can serve as a testing dataset for the research community to validate the evolution of the state-of-the-art for professional vehicle damage  
Figure 9: Evaluation with our public dataset TartesiaDS.  
appraisers. Our ensemble model achieves a very good balance between false positive rate; a metric that is very important for insurance companies, given that appraisers must verify every positive instance, precision, recall, AUC, and inference time in CPU, which is the better option to scale the system in the cloud at the best price.  
Our solution, compared to the DCN+ model, achieve better metrics, and the inference speed is four times faster, improving the state-of-the-art.  
We also provide an alternative for specific cases where greater ensemble-based detection capacity is required using more up-to-date models such as YOLOv8.  
For future work, we will focus on vehicle damage detection based on videos. For this, we will use spatio-temporal context using transformer-based models such as [54; 55; 56], supported by a new video-based dataset under expert supervision. Relying on works such as [57; 58], we will be able to face the challenges of camera movements, changes in environmental conditions such as rain, lights, changes in the appearance of objects, and above all, achieve the tracking of an object that is often restricted by the instability of a detector. In addition, we will consider implementing utilities to facilitate the construction of a new dataset by generating masks using only bounding boxes and generating damage trajectories using tracking algorithms.  
Conceptualization, J.A.-G., M.A.M.-d.-A., J.L.P.-M. and S.A.P.-Z.; Methodology, J.A.A.-G., M.A.M.-d.-A., J.L.P.-M. and S.A.P.-Z.; Formal analysis, S.A.P.-Z. and D.C.-G.; Investigation, S.A.P.-Z.; Data curation, D.F.-C.; Writing--original draft, S.A.P.-Z.; Writing--review and editing, J.A.A.-G., M.A.M.-d.-A., S.A.P.-Z. and J.L.P.-M.; Supervision, M.A.M.-d.-A., J.A.A.-G. and J.L.P.-M.; Project administration, J.A.A.-G.; Funding acquisition, J.L.P.-M. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the Valora Group with its own funds, with partial support from the HORUS project (grant no. PID2021-126359 OB-100) funded by MCIN/AEI/10.13039/5011000 11033 and by the European Union NextGenerationEU/PRTR. This research was also supported by grants from NVIDIA and used NVIDIA A100 donated to [PERSON].  
Not applicable.  
The data presented in this study can be requested from the corresponding author through the repository. The data are not available to the public for privacy reasons.  
We are grateful for the hard work of the Valora Group's team of experts in developing the dataset. We also thank the HORUS project (grant no. PID2021 126359 OB-100) funded by MCIN/AEI/ 10.13039/501100011033 and the support of [PERSON] for facilitating the use of the NVIDIA A100 resource.  
The authors declare no conflicts of interest.