LoRa-enabled GPU-based CubeSat Yolo Object Detection with Hyperparameter Optimization
Institute of Electrical and Electronics Engineers Inc.
This paper presents a Lora-enabled GPU-based CubeSat Neural-Network Real-Time Object Detection with hyperparameter optimization is presented. When number of epochs increased from 10 to 50 and learning rate tuned to 0.00104, the validation loss improved to 0.018 and training object loss improved to 0.042. Model mean average precision mAP_0.5 is improved to 0.986 and the precision is improved to 98.9%. Epoch and learning rate are traded-off to optimize model accuracy performance. The Lora-enabled CubeSat onboard transceiver provides long range onboard sensors readings providing spaced-based IoT application capability. © 2022 IEEE.
This work is not available in the CUD collection. The version of the scholarly record of this work is published in 2022 International Symposium on Networks, Computers and Communications, ISNCC 2022 (2022), available online at: https://doi.org/10.1109/ISNCC55209.2022.9851761
GPU-based CubeSat, Hyperparameter Optimization, Long-Range Lora, Yolo Neural Network Object Detection
Khatib, Z. E., Mnaouer, A. B., Moussa, S., Abas, M. A. B., Ismail, N. A., Abdulgaleel, F., . . . Ashraf, L. (2022). LoRa-enabled GPU-based CubeSat yolo object detection with hyperparameter optimization. Paper presented at the 2022 International Symposium on Networks, Computers and Communications, ISNCC 2022, https://doi.org/10.1109/ISNCC55209.2022.9851761.