Optimizing Affordable Drone Surveillance with Advanced Image Processing Techniques

Abstract

The widespread adoption of drones can be attributed to their low cost and convenience which led to a growth in their use for surveillance reasons leading to their extensive use in other areas too. In spite of this, the problem of maximizing their production while simultaneously minimizing their expenses is still one that they face. This paper provides a comprehensive methodology that can improve the efficiency of drone surveillance at a cheaper cost. This methodology is accomplished through the application of contemporary image processing technology. The employment of RRDB ESRGAN for the purpose of image enhancement, the utilization of face recognition for the purpose of authentication, the utilization of YOLO for the purpose of object detection, and the streamlining of data collecting and processing are all components of our plan. By enhancing image quality, implementing secure access through facial recognition, and facilitating real-time object detection, our system seeks to maximize drone surveillance, thereby improving both efficiency and accuracy. It has been demonstrated by the findings of this study that drone systems that are less expensive and have been improved by more advanced image processing algorithms have the potential to improve security and surveillance capabilities in a variety of different fields.