Drone/UAVs Surveillance feeds Video Object Recognition Using Transfer Learning

Abstract

The objective of this research is to demonstrate the use of a convolutional neural network (CNN) for object detection (OD) on drone videos (CNN). The goal of the study is to determine how well the YOLO V3 (Y-V3) and feature extractor recognise objects in drone-shot video frames the real-world.According to the study, application of several target identification algorithms to “regular” images captured by “normal” cameras has different efficiency impacts on the quantity, precision, and target performance consumption, as well as when applying the technique to image data collected by drones. Unmanned aerial vehicles (UAVs), a very active domain in this field, are crucial to the implementation of any robot’s complete autonomy due to its enhanced capability of OD. We have conducted numerous experiments to address our functional issues to investigate the efficacy of the most sophisticated target detection algorithm in the image data acquired by UAV. In this study, the algorithm’s performance metrics are tallied after being tested on 1099 photos.