Real-Time Birds Shadow Detection for Autonomous UAVs

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dc.contributor.advisorHajdu, András
dc.contributor.authorBouali, Kassem Anis
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2024-06-23T18:46:58Z
dc.date.available2024-06-23T18:46:58Z
dc.date.created2023-11-16
dc.description.abstractAutonomous unmanned aerial vehicles (UAVs) are commonly used for wildlife exploration and animal monitoring. Therefore, bird attacks pose a significant challenge to UAVs. As we know, Traditional Bird Detection methods used for prevention against attacks may fail when the attacks occur from unobservable angles. However, the UAV can gain an early indication of an impending attack if it detects the location of the bird’s shadow and takes proactive measures to minimize the risk of damage. To address this, we present the ShadowBirdCUB dataset, derived from the CUB-200-2011 Dataset, which is used to train cutting-edge Deep Learning Algorithms for shadow detection. Experimental results using various Deep learning Object Detection (DLOD) models and performance metrics demonstrate promising effectiveness. Although this approach is limited to detecting attacks from certain angles, it is a valuable addition to existing bird detection methods.
dc.description.courseProgramtervező informatikus
dc.description.degreeMSc/MA
dc.format.extent31
dc.identifier.urihttps://hdl.handle.net/2437/374631
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectShadow detection
dc.subjectObject detection
dc.subjectSynthetic data generation
dc.subjectUAVs
dc.subjectDeep learning
dc.subject.dspaceInformatics
dc.subject.dspaceInformatics::Computer Science
dc.titleReal-Time Birds Shadow Detection for Autonomous UAVs
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