Enhancing Unmanned Aerial Vechicle Surveillance: Real Time Drone Detection with Yolov7 Deep Learning

This is an advanced object detection system with YOLOv7 model, implemented for UAV surveillance as outlined in the article. By integrating the system with Roboflow serving as its database manager and data augmentation software, it benefits from a broad array of reputable datasets which are improving training and inferences. In the blog, we cover all steps from model cloning (on TensorFlow) with one/two script line conversion to preprocessing of datasets and work automation for training or evaluation. The optimizations in the implementation of YOLOv7 dog led to scalability, allow more object categories and deploy security for real time surveillance with autonomous navigation. Extensive testing demonstrates that our system exceeds state-of-the-art methods in terms of detection performance as well as efficiency, demonstrating the significant benefits for UAV hotspots gained from a deployment of advanced deep learning techniques.

I. Introduction

Unmanned Aerial Vehicle (UAV) The development of unmanned aerial vehicle technology now enables surveillance and monitoring applications where a diversity in geographical coverage, sensing perspectives and access to data would otherwise never be possible. Nevertheless, the utility of UAV surveillance [1] depends on how quickly and accurately objects within captured imagery can be perceived. That has been the catalyst for a rapid move toward deploying more deep learning techniques to improve detection and tracking. Most notably YOLOv7 has become a big breakthrough in real-time object detection among these models. In this paper focuses the transformative effect of YOLOv7 on UAV surveillance and explain how it can be deployed to identify drones or moving objects using drone detection analysis. Designed to handle challenges unique to UAV surveillance - such as fluctuating object sizes, occlusions and backgrounds typical in aerial imagery - YOLOv7 incorporates advanced methods like attention mechanisms and Bi-directional Feature Pyramid Networks (BiFPN). The result is both a significant increase in the model's mean average precision (mAP) and an enhanced operational capability across multiple domains spanning search-and-rescue; through to environmental monitoring, executed under conditions too varying for traditional methods. Our forcame into the deep learning and UAV surveillance to bring out neater touches with YOLOv7 [2] there by providing an additional edge over usual detection methods. In this paper, we leveraged a set of exhaustive experimental studies to establish the efficacy and applicability of such sophisticated deep learning techniques in improving accuracy, speed and versatility for UAV tracking systems. This is an opening statement to introduce YOLOv7 contribution for UAV surveillance that will lead the upcoming advancements in aerial monitoring technologies.