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Smart city article details
| Title | Real-Time Road Damage Detection System On Deep Learning Based Image Analysis |
|---|---|
| ID_Doc | 44435 |
| Authors | Kulambayev B.; Gleb B.; Katayev N.; Menglibay I.; Momynkulov Z. |
| Year | 2024 |
| Published | International Journal of Advanced Computer Science and Applications, 15, 9 |
| DOI | http://dx.doi.org/10.14569/IJACSA.2024.01509107 |
| Abstract | This research paper introduces a sophisticated deep learning-based system for real-time detection and segmentation of road damages, utilizing the Mask R-CNN framework to enhance road maintenance and safety. The primary objective was to develop a robust automated system capable of accurately identifying and classifying various types of road damages under diverse environmental conditions. The system employs advanced convolutional neural networks to process and analyze images captured from road surfaces, enabling precise localization and segmentation of damages such as cracks, potholes, and surface wear. Evaluation of the model’s performance through metrics like accuracy, precision, recall, and F1-score demonstrated high effectiveness in real-world scenarios. The confusion matrix and loss curves presented in the study illustrate the system’s ability to generalize well to unseen data, mitigating overfitting while maintaining high detection sensitivity. Challenges such as variable lighting, shadows, and background noise were addressed, highlighting the system’s resilience and the need for further dataset diversification and integration of multimodal data sources. The potential improvements discussed include refining the convolutional network architecture and incorporating predictive maintenance capabilities. The system’s application extends beyond mere detection, promising transformative impacts on urban planning and infrastructure management by integrating with smart city frameworks to facilitate real-time, predictive road maintenance. This research sets a benchmark for future developments in the field of automated road assessment, pointing towards a future where AI-driven technologies significantly enhance public safety and infrastructure efficiency. © (2024), (Science and Information Organization). All rights reserved. |
| Author Keywords | convolutional neural networks; Deep learning; image segmentation; infrastructure management; Mask R-CNN; predictive maintenance; real-time analytics; road damage detection; smart cities; urban planning |
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