# Road Damage Detection and Classification with Detectron2 and Faster R-CNN

arXiv: https://arxiv.org/abs/2010.15021 · HTML: https://mlsanity.com/paper/2010.15021/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework](https://mlsanity.com/paper/2309.01469/) (2023)
- [Road Damages Detection and Classification with YOLOv7](https://mlsanity.com/paper/2211.00091/) (2022)
- [Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10](https://mlsanity.com/paper/2410.08409/) (2024)
- [Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms](https://mlsanity.com/paper/2503.18082/) (2025)
- [TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualization](https://mlsanity.com/paper/2101.11266/) (2021)
- [Identifying the exterior image of buildings on a 3D map and extracting elevation information using deep learning and digital image processing](https://mlsanity.com/paper/2201.01081/) (2022)
- [Classification of Beer Bottles using Object Detection and Transfer Learning](https://mlsanity.com/paper/2201.03791/) (2022)
- [Road Disease Detection based on Latent Domain Background Feature Separation and Suppression](https://mlsanity.com/paper/2309.07616/) (2023)
- [Cut-and-Paste with Precision: a Content and Perspective-aware Data Augmentation for Road Damage Detection](https://mlsanity.com/paper/2406.18586/) (2024)
- [Underwater Object Detection Enhancement via Channel Stabilization](https://mlsanity.com/paper/2408.01293/) (2024)
- [Comparative Analysis of Object Detection Algorithms for Surface Defect Detection](https://mlsanity.com/paper/2510.21811/) (2025)

## Abstract (arXiv)

The road is vital for many aspects of life, and road maintenance is crucial for human safety. One of the critical tasks to allow timely repair of road damages is to quickly and efficiently detect and classify them. This work details the strategies and experiments evaluated for these tasks. Specifically, we evaluate Detectron2's implementation of Faster R-CNN using different base models and configurations. We also experiment with these approaches using the Global Road Damage Detection Challenge 2020, A Track in the IEEE Big Data 2020 Big Data Cup Challenge dataset. The results show that the X101-FPN base model for Faster R-CNN with Detectron2's default configurations are efficient and general enough to be transferable to different countries in this challenge. This approach results in F1 scores of 51.0% and 51.4% for the test1 and test2 sets of the challenge, respectively. Though the visualizations show good prediction results, the F1 scores are low. Therefore, we also evaluate the prediction results against the existing annotations and discover some discrepancies. Thus, we also suggest strategies to improve the labeling process for this dataset.