# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 2 Related Works
### Models for Object Detection  
Object detection has been evolving at an impressive pace, with two groups of detectors becoming known: two-stage detectors and single-stage detectors. Two-stage detectors are usually characterized by high accuracy, but they have major limitations in terms of speed. Fast RCNN [5], faster R-CNN [6] and cascade RCNN [7] are some of the most popular two-stage models used in the task of object detection. However, new models have emerged that surpassed the state-of-the-art, such as InternImage [8], which explores the use of deformable convolutions (DCN) [9] to address large-scale model training and M3I pre-training [10], which is a one-stage pre-training paradigm, integrating pre-training methods such as supervised pre-training, weakly supervised pre-training, and self-supervised pre-training. This is leveraged to train the InternImage model.  
On the other hand, single-stage detectors have been presented as an alternative to two-stage detectors, since they can perform inference in a single step and can be executed in real-time. Some single-stage models that stand out are the SSD [11], where two techniques are introduced: the multi-reference technique and the multi-resolution technique. The multi-reference technique consists of defining a set of references in each location of the image to subsequently predict the detections based on these references. The multi-resolution technique allows for the detection objects of different scales in different layers of the network. Next, RetinaNet [12] is proposed with the goal of reducing the accuracy gap between single-stage detectors and two-stage detectors. The authors of RetinaNet found that the main problem was in the extreme imbalance present between the foreground and background classes at the time of training. To solve the imbalance problem, they created a new function called focal loss, which is based on a transformation of the standard cross-entropy loss that allows the detector to focus on misclassified examples during training.  
Finally, to address real-time object detection, the YOLO family models are proposed [13; 14; 15; 16], including Ultralytics ([[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)]([https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)), accessed on 16 October 2024) YOLOv8. Those models have allowed researchers to address problems such as human behavior analysis [17], autonomous driving [18], traffic assessment [19], multiple target tracking [20], medical diagnostic analysis [21], and detection of road defects by means of a light model applied in real time [22], characterized by high speed and accuracy.  
In our approach, we will use an ensemble of 10 YOLOv5 models to achieve a high-precision model [23]. YOLOv8 gives us lower performance compared to YOLOv5, so we chose YOLOv5 as our base detector. Our intuition about the better performance of YOLOv5 with respect to YOLOv8 is that the former is better adapted to real scenarios. This has been shown in the literature [24; 25], where YOLOv5 outperforms YOLOv8 in terms of recall in particular cases and additionally generates higher reliability by having a low standard deviation. However, we extend the use of our ensemble to YOLOv8 models for the purpose of comparing the performance of more recent models.