Abstract:
The increasing prevalence of drones alongside security threats related to these in various
sectors, including military, surveillance, and delivery, has underscored the urgent need for
advanced drone detection and tracking systems (DDTS) capable of efficiently operating in
diverse environmental conditions. This research addresses the limitations of existing object
detection models by developing a specialized novel dataset in LWIR (Long-Wave Infrared)
and SWIR (Short-Wave Infrared) spectrum which was processed through various data augmentation techniques to improve drone detection and tracking capabilities under varying
illumination conditions. To improve computational efficiency and detection accuracy, several
variants of the YOLOv10 model including n,s,m,b,l, and x are optimized using advanced
techniques like Ghost Convolutions, Pruning, TensorRT, and ONNX configurations for CPU
and GPU of advanced computer and edge-devices including Jetson Orin Nano and Jetson Orin
AGX. The comparative evaluations of various tracking algorithms are conducted alongside
the optimized YOLO models, revealing that Botsort is the most effective algorithm for real time drone tracking due to its superior performance in dynamic environments.
The optimized YOLO models across various hardware configurations were compared based
on performance metrics like inference speed, frames per second (FPS), mean average
precision, recall, F1 score, and accuracy. The Ghost Convolutions incorporated YOLO models
outperformed their standard models with much fewer parameters making them ideal for real time applications. The findings of this research not only contribute to the existing body of
knowledge in the field of computer vision and object tracking but also provide practical
guidelines for implementing effective drone detection and tracking systems (DDTS) to
improve security and public safety.
