Detection Neural networks are memory intensive, making them difficult to deploy on embedded system with limited hardware resources. To solve the real-time problem of airborne recognition, we introduce a lightweight detection network framework based on the YOLOv3-tiny, that work together to reduce the volume of network by 3× with accuracy increasing by 1% and detection speed reaching 15 fps. The method is based on the YOLOv3-tiny, the size of which is 33.1 MB. Its reference time and mAP are 0.085 s and 78.6%, respectively. Next, we use the structure compression and improved channel pruning strategy to reduce the network volume to 10.33 MB without affecting their accuracy. Considering that model compression will have an impact on the performance of the network, we also adopt some tricks, such as adjusting learning rate and adding attention mechanism, to further improve the identification accuracy, from 78.6% to 79.3%. These measures enable the model to run better and faster on the resource-constrained airborne embedded devices or mobile applications.
A Realtime Lightweight Detection Network Framework for UAV Identification
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 263 ; 2679-2688
2022-03-18
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Realtime iMPI-guided PTA with a lightweight human-sized MPI scanner
DataCite | 2023
|A Realtime Neural Network in Space
British Library Conference Proceedings | 1998
|Realtime Object Detection and Position Measurement
British Library Conference Proceedings | 2005
|Ideogram Identification in a Realtime Traffic Sign Recognition
British Library Conference Proceedings | 1995
|