UAV Detection Systems to Challenging No-fly Zone Scene Activity Recognition
Ref: CISTER-TR-241103 Publication Date: 6 to 8, Nov, 2024
UAV Detection Systems to Challenging No-fly Zone Scene Activity Recognition
Ref: CISTER-TR-241103 Publication Date: 6 to 8, Nov, 2024Abstract:
We presented the thesis research poster, "UAV Detection Systems to Challenging No-fly Zone Scene Activity Recognition," in the CISTER's Workshop on Cyber-Physical Systems. We presented two different methodologies for static vision system models using day and night vision cameras. The vision systems are "Fusion flow-enhanced graph pooling residual networks for unmanned aerial vehicles surveillance in day and night dual visions" and "A hybrid deep learning model for UAVs detection in day and night dual visions". These systems are capable of detecting UAVs in different complex environmental effects in any light variation scenario. The models successfully tested with unseen data and achieved remarkable results for the no-fly zone area monitoring.
Alam Noor
Kai Li
Luís Almeida
Eduardo TovarPoster presented in The 32nd International Conference on Real-Time Networks and Systems (RTNS 2024), CISTER's Workshop on Cyber-Physical Systems.
Porto, Portugal.
DOI:https://cister-labs.pt/rtns24/cwcps.
Record Date: 12, Nov, 2024