| dc.description.abstract | In the modern military setting, fast
improvements in drone technology have transformed
warfare strategies, providing key advantages in
reconnaissance, surveillance, and special operations.
Sri Lanka's diversified and difficult geography has
presented unique security concerns that demand
advanced defensive technology. With the key objective
of developing an object detection method utilizing
neural networks, this study examined the integration
of Unmanned Aerial Vehicles (UAVs) equipped with
advanced object-detecting techniques into Sri Lanka's
military structure. Here, object detection in UAV
imaging has utilized YOLOv8 (You Only Look Once
Version 8), a deep learning model which is known for
its accuracy and real-time processing capabilities for
recognizing objects of interest. An adapted aerial
object identification dataset and a questionnaire were
used in this study to assess YOLOv8's performance in
a range of operational circumstances. The way how
this model detects a variety of objects in a range of
environmental conditions was evaluated. The study
also analysed the ethical consequences, operational
issues, and technological difficulties of using UAVs for
military reconnaissance. Through integrating modern
object detection technology on UAVs with deep
learning language, it is possible to enhance Sri
Lanka's military capabilities. The effectiveness of
YOLOv8 model, which is well-known for its accuracy
and real-time processing in boosting national security
have been analysed and discussed. By addressing the
technological considerations in the use of UAVs
technologies, this study may offer robust defence plans
customized to the unique security and geographic
conditions of the Sri Lankan Military context. | en_US |
