University of Twente Student Theses
Vehicle detection and classification using passive multi-modal sensing and TinyML
Jelsma, S.G. (2024) Vehicle detection and classification using passive multi-modal sensing and TinyML.
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| Full Text Status: | Access to this publication is restricted |
| Embargo date: | 12 December 2027 |
| Abstract: | This research evaluated TinyML for the classifi- cation and localization of military vehicles. A measurement platform with seismic, acoustic, and magnetic capabilities was used for data collection. A multi-modal ML model showed an 87% classi- fication accuracy at 100m with a model size of 527kB. Using quantization, the model was further reduced to 141kB, with 82% classification accu- racy. A microphone array was used for wideband acoustic beamforming, which was interpreted us- ing an ML model, resulting in a mean average er- ror of < 20o with a range of 50m. The a model size of 154kB was reduced to 48kB after quantization, with < 1% increase in error. |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 33 physics, 50 technical science in general, 53 electrotechnology |
| Programme: | Robotics MSc (60973) |
| Link to this item: | https://purl.utwente.nl/essays/104696 |
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