Dynamic Object Detection and Tracking System on Unmanned Aerial Vehicles for Surveillance Applications Using RegionViT-Based Adaptive Multi-Scale YOLOv8

Abstract

In general, the object detection mechanism recognizes target objects in the image frames, and the tracking mechanism helps to capture the movement of target objects in diverse frames. Recent developments in Artificial Intelligence (AI) have enabled us to build computers, robots, and automated tools that are mostly designed for performing tasks and generating decisions without human assistance. The drones called Unmanned Aerial Vehicles (UAVs) are employed for many kinds of objectives, including parcel shipping, rescue operations, recognizing objects, and monitoring. Object detection and tracking systems are crucial for UAVs because they help to optimally capture moving objects, areas, and threats for enhancing security, surveillance, and awareness at an earlier stage. Also, they help to ultimately predict the category and location of UAVs from the video frames. Many researchers have developed diverse object detection and tracking methods on UAVs; yet, it is complex for continuously monitoring small objects in the gathered data, and it is affected by noise and blurriness due to the motion of UAVs. One of the greatest challenging duties for UAVs is object recognition and tracking since it needs precise, swift, and cost-effective object detection and tracking. Pre-trained networks are required for the detection of objects based on deep learning. Mismatches between the pre-trained and the target domain network areas create problems in object detection. With the aim of resolving these issues, a deep learning-assisted UAV control mechanism is developed in this research work by performing detection and tracking of objects. The developed model is helpful in improving rescue operations in disaster areas and security surveillance. At first, the input videos are accumulated from benchmark sources. From the videos, the resolution of the images is studied for an accurate object detection procedure. Next, the detection and tracking of the object are done via the developed Region Vision Transformer-based Adaptive Multi-scale You Only Look Once v8 (RV-AMYOLOv8). The parameters fromYOLOv8 are optimized using the Fitness-based Random Variable for Elk Herd Optimizer (FRVEHO) for enhancing the performance. The quantitative outcomes of the implemented approach help to analyze the suggested network's performance with conventional techniques. Here, the developed method attains 96% accuracy and 95% of precision measure to demonstrate its better performance than the existing methods. This object detection is helpful for analyzing the surrounding obstacles while controlling the UAVs.