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.

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.

Analyzing vehicle movement trajectories is essential for understanding urban mobility and traffic flow patterns. Obtaining a reliable estimate of vehicle trajectory is challenging as it requires the vehicle to be observed and re-identified at different locations and times. Recently, a scalable citywide traffic flow estimation method has been proposed utilizing moving cameras on vehicle dashboards. This study extends the recently proposed method for traffic flow estimation by using cameras mounted on multiple moving observers to reconstruct the trajectory of detected vehicles. We develop the CARLA ReID dataset, which includes more than 50,000 images taken from 85 cameras for over 700 different vehicle models, and train a re-identification network to identify the same vehicle by multiple observers. Utilizing our proposed methodology, we conduct extensive research to estimate trajectories of vehicles in a driving simulator CARLA and evaluate the accuracy of reconstructed trajectories using Symmetrized Segment-Path Distance (SSPD) and Hausdorff Distance metrics. Our proposed method achieves a mean error of 5.13 meters evaluated using the SSPD metric for ten driving experiments in CARLA. Findings from this study will provide valuable insights for conducting traffic flow research in a simulation environment, which is otherwise challenging and costly in practice.

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.

Analysis of traffic fl ow pa rameters is ne cessary for Intelligent Transportation Systems (ITS) and autonomous driving research. Deep learning-based vehicle detection techniques have been widely used in reconstructing traffic fl ow parameters from video images. This research proposes a novel cross-sectional traffic fl ow es timation al gorithm to re construct tr affic volume from moving camera videos. We develop a vehicle detection dataset with more than one million annotations of vehicles with orientation and train a YOLOv4 based object detection network. We leverage the accurate vehicle detection model in tracking and estimating the distance of detected vehicles using Simple Online and Realtime Tracking (SORT) and photogrammetry techniques. The estimated distances and forward bearing of the observing vehicle are then utilized to calculate the GPS position of detected vehicles and used in the algorithm to estimate cross-sectional traffic fl ow. We ut ilize th e pr oposed al gorithm to es timate the traffic flow of 580 OpenStreetMap (OSM) road links and achieve an average accuracy of 84.30% verified a gainst 1 1 t raffic police sensor data in Susono city in Japan. The proposed large-scale dataset and cross-sectional traffic flow estimation algorithm open new avenues for ITS and autonomous driving research.

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.