# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 2 Related Works
### Damage Detection  
To move from the domain of object detection to vehicle damage detection, it is only necessary to consider damage types as object classes. In [26], the authors chose the most common types of damage such as bumper dents, door dents, glass breakage, headlight breakage, scratches, and crushing. They experimented with various techniques to train CNNs, such as direct training and transfer learning. In [2], the authors opted for the approach of dividing damage assessment into three subtasks: identification, localization, and severity level. By using two CNN models, VGG16 and VGG19, they managed to achieve an accuracy of 95.22% in identification, 76.48% in localization, and 58.48% in damage severity level. In [27], the authors divide their implementation into two main parts. The first one consists of verifying if the vehicle in an image is damaged, then two additional models are used to obtain the location and severity level of the damage. The second part consists of using mask RCNN [28] to mask the damaged region. The authors compared variousmodels such as VGG16, VGG19, Resnet50, and InceptionV3, and VGG16 provided the best results. In [29], the authors opted for a one-stage implementation to mark the damage box and identify its type. The model they used was YOLOS [30], characterized by the use of transformers. On the other hand, in a more recent work [31], the authors opted for the implementation of a YOLOv5-type detector trained on a proprietary dataset and succeeded in adapting it to the problem of damage detection. These previous approaches are similar to the one proposed in this work, as we use our own dataset, but such datasets usually lack complexity, such as the presence of small and irregularly shaped frictions. Therefore, these works allow us to identify the ability of one-stage models to address the problem of vehicle damage detection, but the need to implement a method to minimize false positives is evident. On the other hand, in [32], an RCNN-type object detector [33] is used to identify the possible location of the damage and complement it with a RCNN mask-type detector to define the exact area of the damage. Similarly, in [33], a two-step workflow is applied: the combination of the segmentation of the parts of a vehicle, together with the segmentation of the damage. Two mask RCNN detectors were used to perform this workflow, and it should be noted that the use of segmentation models often requires high costs for a company to create a dataset focused on segmentation. Previous works [34; 35] address damage quantification by implementing a rule-based system based on the detection of the damage level and structured information such as detected parts, price associated with a part, repair costs, etc. This approach complements object detection and takes a step towards damage quantification. The quantification of damage has an important limitation, as the costs and methods of repair work in different countries may differ significantly. The quantification of damages is therefore outside the scope of this research. Finally, DCN+ [4], an improved version of DCN including multiscale learning and focal loss techniques, marks the current state-of-the-art in damage detection. In the field of vehicle damage detection, the state-of-the-art uses one- or two-stage models but not ensembles, which have been used in other fields with considerable success [36; 37]. Although in [38; 39], the implementation of ensemble learning for object detection is proposed and guidelines are established, for the detection of damage to vehicles, it is necessary to implement additional steps such as searching for the best thresholds for each model and simplifying the non-maximum suppression algorithm by merging boxes. Based on the work mentioned above, the use of single-stage models has the risk of generating a false positive rate not allowed by insurance companies.