Analysis Performance of One-Stage and Two Stage Object Detection Method for Car Damage Detection

The large use of private cars is directly proportional to the number of insurance claims. Therefore, insurance companies need a breakthrough or new approach that is more effective and efficient to be able to compete for the trust of their customers. One approach that can be taken is to use artificial intelligence to detect damage to the car body to speed up the claims process. In this research, several experiments will be carried out using various types of models, namely Mask-R-CNN, ResNet50, MobileNetv2, YOLO-v5, and YOLO-v8 to detect damage to the car body. Furthermore, in the experiments that were carried out, the best results were obtained using the YOLO-v8x model with precision, recall, and F1-score values of 0.963, 0.951, and 0.936 respectively.

@article{Setyawan2024,
  title     = {Analysis Performance of One-Stage and Two Stage Object Detection Method for Car Damage Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Harum Ananda Setyawan and Alhadi Bustamam and Rinaldi Anwar Buyung},
  doi       = {10.14569/IJACSA.2024.01507103},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507103}
}