Deep Learning and Fuzzy Logic-Based Hybrid Framework for Aerial Object Tracking

OpenAlex İngilizce

This study introduces an aerial target detection and tracking system using the YOLOv8 model combined with the ByteTrack algorithm. Our key system is built on improving accuracy and efficiency in the detection and tracking of aerial objects in video frames with the use of a specially trained YOLOv8 model. ByteTrack further completes the tracking in a robust manner, even for dynamically changing environments. Our scheme also embeds a new decision-making layer by using a Fuzzy Logic System, which adopts Trapezoidal Membership Functions for confidence and distance evaluation of detected objects to achieve priority levels in tracking. For prediction of future positions, a Kalman Filter is performed on these. This enhances the ability of the system to foresee various situations. Different scenarios of the system effectively demonstrate how the system can dynamically prioritize and track multiple objects in accordance with their threat level and proximity. The findings of this study could contribute to existing methods that not only improve detection and tracking accuracy but also incorporate a decision-making process similar to humans. Hence, its highly applicable domains are defense and surveillance, which require real-time and accurate threat assessment. This system can work under different operational conditions reliably and thus may provide a valid tool for enhancing airspace security.