This paper summarizes the Optimized Road Damage Detection Challenge (ORDDC’2024), a Big Data Cup featured at the IEEE International Conference on Big Data 2024. Building on previous competitions, ORDDC’2024 aims to enhance the automatic detection and classification of road damage from images. It introduces two novel contributions: first, a standardized platform for model deployment that ensured consistent performance evaluation across all participants; second, an emphasis on inference speed as a critical evaluation criterion to meet the growing demand for real-time applications in infrastructure monitoring. The competition utilized the road damage dataset, RDD2022, released through the Crowdsensing-based Road Damage Detection Challenge CRDDC’2022, comprising road images from majorly 6 countries: India, Japan, Czech Republic, Norway, United States, and China. Attracting participants from 19 countries, the challenge yielded 76,069 submissions in Phase 1 and 353 in Phase 2. The winning model offers two solutions: one optimized for accuracy, achieving a peak F1-score of 86.18% at an inference speed of 136.41 milliseconds per image, and another optimized for speed, delivering 26.8 milliseconds per image with an F1-score of 79.27%. This paper analyzes leading solutions and challenges faced, providing insights for enhancing real-time road damage detection and improving global infrastructure maintenance strategies.

There is an urgent need for innovative technologies to detect road damage efficiently and cost-effectively. Traditional centralized deep learning models face challenges due to extensive data transfer and privacy concerns when sharing data among different parties. Federated Learning (FL) addresses these issues by sharing model parameters instead of raw data, enhancing collaboration without compromising privacy. While previous studies focused on similar datasets from various countries, this research showcases FL's efficacy with diverse data from Japan, China, and Norway. Our proposed solution involves developing a multi-country federated model capable of learning from diverse datasets and enhancing road damage detection accuracy across different regions, making it more robust and generalized than traditional models. Using YOLOv8l and the Flower framework with the FedAvg strategy, the FL model achieved an mAP50 of 0.467 on a multi-country test dataset, outperforming Japan's centralized model by 3.6% and Norway's and China's models by over 20%.

The highly accurate retrieval of large-scale road surfaces is crucial when developing nationwide digital twins. Many existing approaches have focused on detecting road surfaces from high-resolution aerial images, videos captured by dashcams, or digital surface models (DSMs), either individually or in combination with multiple data sources for stronger guarantees. The results of these implementations are highly dependent on the quality of the original data sources that are selected. Additionally, such approaches primarily employ deep learning, which is associated with challenges such as inefficient training and poor generalization of model results. Therefore, in this study, we propose a method for developing wide-ranging pavement surface models by realizing an accurate three-dimensional reconstruction module based on images with a lower resolution and introduce a multilevel Markov random field (MRF) model for mapping road networks from reconstructed sparse point cloud scenes. The experimental results demonstrated that the proposed development pipeline achieved an average of state-of-the-art performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) dense matching dataset with a satisfactory root mean square error (RMSE) and obtained an overall accuracy (OA) of 90.3% and a mean intersection over union (mIoU) of 82.3% in the segmentation task. Using the Geospatial Information Authority of Japan (GSI) aerial image dataset, the robustness of the method in developing road surface models over a wide range of scenarios was further proven while accommodating less stricter source data requirements.

This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data’2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a real-time online evaluation system for the participants. In the presented case, the data constitute 47,420 road images collected from India, Japan, the Czech Republic, Norway, the United States, and China to propose methods for automatically detecting road damages in these countries. More than 70 teams from 19 countries registered for this competition. The submitted solutions were evaluated using five leaderboards based on performance for unseen test images from the aforementioned six countries. This paper encapsulates the top 11 solutions proposed by these teams. The best-performing model utilizes ensemble learning based on YOLO and Faster-RCNN series models to yield an F1 score of 76% for test data combined from all 6 countries. The paper concludes with a comparison of current and past challenges and provides direction for the future.

Road rutting is a severe road distress that can cause premature failure of the road incurring early and costly maintenance costs. Research on road damage detection using image processing techniques and deep learning are being actively conducted in the past few years. However, these researches are mostly focused on the detection of cracks, potholes, and their variants. Very few research has been done on the detection of road rutting. This paper proposes a novel road rutting dataset comprising 949 images and provides both object-level and pixel-level annotations. Object detection models and semantic segmentation models were deployed to detect road rutting on the proposed dataset, and quantitative and qualitative analysis of model predictions were done to evaluate model performance and identify challenges faced in the detection of road rutting using the proposed method. Object detection model YOLOXs achieves mAP@IoU=0.5 of 61.6% and semantic segmentation model PSPNet (Resnet-50) achieves IoU of 54.69 and accuracy of 72.67, thus providing a benchmark accuracy for similar work in future. The proposed road rutting dataset and the results of our research study will help accelerate the research on the detection of road rutting using deep learning.

This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data’2020. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from several countries registered for this competition. The submitted solutions were evaluated using two datasets test1 and test2, comprising 2,631 and 2,664 images. This paper encapsulates the top 12 solutions proposed by these teams. The best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2. The paper concludes with a review of the facets that worked well for the presented challenge and those that could be improved in future challenges.