# Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone

arXiv: https://arxiv.org/abs/1801.09454 · HTML: https://mlsanity.com/paper/1801.09454/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2018.

## Built on by (most cited first, 30 of 45)

- [RDD2022: A multi-national image dataset for automatic Road Damage Detection](https://mlsanity.com/paper/2209.08538/) (2022)
- [Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms](https://mlsanity.com/paper/2204.13590/) (2022)
- [Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)](https://mlsanity.com/paper/2211.11362/) (2022)
- [Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach](https://mlsanity.com/paper/2510.03797/) (2025)
- [Damage detection using in-domain and cross-domain transfer learning](https://mlsanity.com/paper/2102.03858/) (2021)
- [Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets](https://mlsanity.com/paper/2408.05649/) (2024)
- [Learning to Automatically Catch Potholes in Worldwide Road Scene Images](https://mlsanity.com/paper/2105.07986/) (2021)
- [Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey](https://mlsanity.com/paper/2402.04297/) (2024)
- [YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism](https://mlsanity.com/paper/2406.11254/) (2024)
- [Real-Time Pothole Detection Using Deep Learning](https://mlsanity.com/paper/2107.06356/) (2021)
- [Efficient Non-Compression Auto-Encoder for Driving Noise-based Road Surface Anomaly Detection](https://mlsanity.com/paper/2111.10985/) (2021)
- [Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data](https://mlsanity.com/paper/2202.00050/) (2022)
- [Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines](https://mlsanity.com/paper/2103.06467/) (2021)
- [AI Driven Road Maintenance Inspection](https://mlsanity.com/paper/2106.02567/) (2021)
- [Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime](https://mlsanity.com/paper/2211.15961/) (2022)
- [Classifying Bicycle Infrastructure Using On-Bike Street-Level Images](https://mlsanity.com/paper/2410.19194/) (2024)
- [Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systems](https://mlsanity.com/paper/2506.14096/) (2025)
- [CNN Model & Tuning for Global Road Damage Detection](https://mlsanity.com/paper/2103.09512/) (2021)
- [Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones](https://mlsanity.com/paper/2202.01421/) (2022)
- [Iterative Self Knowledge Distillation -- From Pothole Classification to Fine-Grained and COVID Recognition](https://mlsanity.com/paper/2202.02265/) (2022)
- [AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage](https://mlsanity.com/paper/2210.03570/) (2022)
- [Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection](https://mlsanity.com/paper/2309.06747/) (2023)
- [CrashCar101: Procedural Generation for Damage Assessment](https://mlsanity.com/paper/2311.06536/) (2023)
- [RDD4D: 4D Attention-Guided Road Damage Detection And Classification](https://mlsanity.com/paper/2501.02822/) (2025)
- [Data-driven Detection and Evaluation of Damages in Concrete Structures: Using Deep Learning and Computer Vision](https://mlsanity.com/paper/2501.11836/) (2025)
- [Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms](https://mlsanity.com/paper/2503.18082/) (2025)
- [M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data](https://mlsanity.com/paper/2504.10123/) (2025)
- [Deep Transfer Learning for Identifications of Slope Surface Cracks](https://mlsanity.com/paper/2108.04235/) (2021)
- [A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management](https://mlsanity.com/paper/2202.13285/) (2022)
- [Hybrid Optimized Deep Convolution Neural Network based Learning Model for Object Detection](https://mlsanity.com/paper/2203.00869/) (2022)

## Abstract (arXiv)

Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body need to repair such damage, they need to clearly understand the type of damage in order to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. In order to generate this dataset, we cooperated with 7 municipalities in Japan and acquired road images for more than 40 hours. These images were captured in a wide variety of weather and illuminance conditions. In each image, we annotated the bounding box representing the location and type of damage. Next, we used a state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/).