FPGA Onboard Processing of Tiny-ML Models Using Radar-Sensing for Oil Spill Monitoring

Deep learning models have been widely used recently for environmental monitoring. Unless designed carefully, they are well known for their large computational complexity and long computation time, which limit their direct utilization as onsite embedded solutions. Therefore, to assure their suitability for onboard processing on drones during tactical responses, we propose in this paper energy-efficient and highly accurate tiny machine learning (TinyML) models in the field of radar remote sensing for oil spill monitoring. More precisely, the proposed signal processing models are based on artificial neural networks and use radar signals to detect oil slicks on top of the seawater and estimate their thicknesses using drones in calm ocean conditions. Despite their extremely small size (7-37 Bytes), their accuracy in detection and parameter estimation exceeds 94%. Moreover, the proposed models are characterized by low-power (32-173 mW) and low-latency (0.35-0.59 µs) performance when implemented on the FPGA computing platform.