TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing
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 architec...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01981-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849769336197087232 |
|---|---|
| author | Amna Khatoon Weixing Wang Mengfei Wang Limin Li Asad Ullah |
| author_facet | Amna Khatoon Weixing Wang Mengfei Wang Limin Li Asad Ullah |
| author_sort | Amna Khatoon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-afb63c6b99724ec387cdc0a5622a3dba |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-afb63c6b99724ec387cdc0a5622a3dba2025-08-20T03:03:27ZengNature PortfolioScientific Reports2045-23222025-07-0115112010.1038/s41598-025-01981-5TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensingAmna Khatoon0Weixing Wang1Mengfei Wang2Limin Li3Asad Ullah4School of Information Engineering, Chang’an UniversitySchool of Information Engineering, Chang’an UniversityCollege of Electrical and Electronic Engineering, Wenzhou UniversityCollege of Electrical and Electronic Engineering, Wenzhou UniversitySchool of Information Engineering, Eurasia UniversityAbstract 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.https://doi.org/10.1038/s41598-025-01981-5TinyMLRoad anomalyFuzzy inference systemRemotely sensed road imageEdge computingRoad network extraction |
| spellingShingle | Amna Khatoon Weixing Wang Mengfei Wang Limin Li Asad Ullah TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing Scientific Reports TinyML Road anomaly Fuzzy inference system Remotely sensed road image Edge computing Road network extraction |
| title | TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing |
| title_full | TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing |
| title_fullStr | TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing |
| title_full_unstemmed | TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing |
| title_short | TinyML-enabled fuzzy logic for enhanced road anomaly detection in remote sensing |
| title_sort | tinyml enabled fuzzy logic for enhanced road anomaly detection in remote sensing |
| topic | TinyML Road anomaly Fuzzy inference system Remotely sensed road image Edge computing Road network extraction |
| url | https://doi.org/10.1038/s41598-025-01981-5 |
| work_keys_str_mv | AT amnakhatoon tinymlenabledfuzzylogicforenhancedroadanomalydetectioninremotesensing AT weixingwang tinymlenabledfuzzylogicforenhancedroadanomalydetectioninremotesensing AT mengfeiwang tinymlenabledfuzzylogicforenhancedroadanomalydetectioninremotesensing AT liminli tinymlenabledfuzzylogicforenhancedroadanomalydetectioninremotesensing AT asadullah tinymlenabledfuzzylogicforenhancedroadanomalydetectioninremotesensing |