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

Amna Khatoon¹, Weixing Wang², Mengfei Wang³

¹School of Information Engineering, Chang'an University, Xi'an, 710064, Shaanxi, China.

Scientific Reports

|July 2, 2025

Summary

This study introduces Tiny Machine Learning (TinyML) with fuzzy logic for efficient road anomaly detection on edge devices. The TinyML-U-Net-FL framework achieves high accuracy, enhancing road safety and intelligent transportation systems.

Keywords:

Edge computing Fuzzy inference system Remotely sensed road image Road anomaly Road network extraction TinyML

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Area of Science:

Computer Science
Remote Sensing
Artificial Intelligence

Background:

Road networks are expanding, increasing complexity and the need for advanced anomaly detection.
Existing methods often have high computational demands and limited real-time capabilities.

Purpose of the Study:

To develop an efficient road anomaly detection framework for resource-constrained environments.
To integrate Tiny Machine Learning (TinyML), remote sensing, and fuzzy logic for improved detection and classification.

Main Methods:

A novel fully connected U-Net architecture (TinyML-U-Net-FL) was developed.
Model compression, quantization, and pruning techniques were employed for efficiency.
Fuzzy logic was incorporated to enhance robustness and precision.

Main Results:

The framework achieved a recall of 92.4%, precision of 78.2%, and F1-Score of 84.7% on DeepGlobe and Dubai datasets.
Demonstrated superior performance compared to contemporary methods like DCS-TransUperNet and GCBNet.
Fuzzy logic integration significantly improved the reliability of anomaly classification.

Conclusions:

The TinyML-U-Net-FL framework offers precise, energy-efficient, and timely road anomaly detection.
This research advances intelligent transportation systems, infrastructure management, and autonomous navigation.
The approach is suitable for real-time analysis on edge devices.