RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis - Ashraf, Arselan; Sophian, Ali; Shafie, Amir Akramin; Gunawan, Teddy Surya; Ismail, Norfarah Nadia; Bawono, Ali Aryo (IEEE Xplore, 2024)
In rapidly urbanizing regions, maintaining road infrastructure integrity is a critical challenge due to increasing vehicular stress and environmental factors. This study introduces the Road Crack Dataset-International Islamic University Malaysia (RCD-IIUM), designed to enhance road pavement infrastructure management in Malaysia. Employing advanced data collection technologies, including high resolution digital imaging, the dataset captures detailed anomalies in road surfaces, laying the groundwork for robust infrastructure analysis. The utility and efficacy of the RCDIIUM dataset were evaluated through the deployment of three deep learning models: Customized YOLOv7, YOLOv8X-SEG, and an Advanced Hybrid Deep Learning Model. These models were tested for their ability to detect and classify road cracks using metrics such as precision, recall, F1-score, and overall accuracy. Results indicated that the YOLOv8X-SEG model outperformed others, demonstrating higher accuracy of 90% and F1-score of 95%. The Customized YOLOv7 model achieved a precision of 93%, recall of 91.58%, and overall accuracy of 88%. The Advanced Hybrid Deep Learning Model achieved a precision of 88%, recall of 89%, F1-score of 88.5%, and overall accuracy of 85%, further validating the robustness of the dataset. The dataset not only bolsters road pavement maintenance strategies but also supports data-driven decision making for urban planning and policymaking.