TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing - Amna Khatoon; Weixing Wang; Mengfei Wang; Limin Li; Asad Ullah (Scientific Reports)
Abstract 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.