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| Title | Orddc'2024: State Of The Art Solutions For Optimized Road Damage Detection |
|---|---|
| ID_Doc | 40982 |
| Authors | Arya D.; Omata H.; Maeda H.; Sekimoto Y. |
| Year | 2024 |
| Published | Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 |
| DOI | http://dx.doi.org/10.1109/BigData62323.2024.10825254 |
| Abstract | 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. © 2024 IEEE. |
| Author Keywords | Big Data; Classification; Deep Learning; Intelligent Transport; Object Detection; Open International Research Community; Road Maintenance; Smart City Applications |
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