TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing.

Advanced techniques for detecting and classifying road anomalies are crucial due to road networks' rapid expansion and increasing complexity. This study introduces a novel integration of Tiny Machine Learning (TinyML), remote sensing, and fuzzy logic through a fully connected U-Net architecture, TinyML-U-Net-FL, tailored for anomaly detection in resource-constrained environments. Our framework addresses critical gaps in existing methodologies, such as high computational demands and limited real-time processing capabilities, by leveraging model compression, quantization, and pruning techniques. These enhancements facilitate efficient real-time analysis directly on edge devices. In rigorous evaluations using the DeepGlobe and Dubai aerial imagery datasets, our framework achieved a notable recall of 92.4%, precision of 78.2%, and an F1-Score of 84.7%, demonstrating superior performance compared to contemporary methods, including DCS-TransUperNet, GOALF, GCBNet, DiResNet, and ScRoadExtractor. Incorporating fuzzy logic significantly improves the robustness of anomaly detection, enabling more precise and reliable classification. This research contributes substantially to intelligent transportation systems by facilitating precise, energy-efficient, timely detection and classification of road network irregularities, enhancing infrastructure management road safety, and supporting autonomous navigation applications.