Towards Real Time Object Detection in UAV

Real-time object detection in Unmanned Aerial Vehicles (UAVs) is a critical challenge due to the constraints of limited computational resources, high-speed motion, and dynamic environments. This study explores advanced lightweight deep learning models and optimization techniques to enable efficient and accurate object detection on UAV platforms. By leveraging edge computing and model compression, we aim to achieve real-time performance without compromising detection accuracy. Experimental results demonstrate the feasibility of deploying these models on embedded systems, such as Raspberry Pi, for real-time applications. This work contributes to enhancing the autonomy and operational capabilities of UAVs in various fields, including surveillance, search and rescue, and environmental monitoring.