International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
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Real-time object detection and video monitoring in Drone System Ahmad Bilal Zaidi1, Sadaf Zahera2 1Student, Deptt. Of Computer Engineering, Zakir Husain College of Engineering and Technology,
Aligarh Muslim University
2Student, Deptt. Of Computer Engineering, Zakir Husain College of Engineering and Technology,
Aligarh Muslim University ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This research paper investigates real-time
finding, scene assessment, crowd monitoring, segmentation, image captioning and activity recognition are key elements of a wide range of extremely complex computer vision tasks. Despite significant progress in developing broad object detection systems that can distinguish a wide range of items, there is still a need for precise and efficient object detection in the context of drone applications [14].
object detection and video monitoring in drone systems, with a focus on traditional computer vision algorithms and deep learning algorithms. Traditional computer vision algorithms such as Haar cascades, HOG, template matching, edge detection, and optical flow are explored in the first section, while the second section focuses on deep learning algorithms, specifically region-based detection and YOLO.
Drones are becoming more and more popular in a vast range of timely applications such as surveillance [26], delivery services [27], traffic tracking [28], agriculture [29], disaster management [30], and maritime security [31]. Amazon, for example, has been given federal authorisation to deploy drones as part of its delivery service and there are reports that drones may be an acceptable means of transporting medicinal products in rural areas. In the area of precision farming, drones are also expected to have a significant impact since they can assist farmers in tasks such as crop monitoring, analyses, and management, including selection of effective pesticides and optimisation of water supply. DJI, the world's leading drone maker, is developing drones that are equipped with sensors specific to protect agricultural crops from insects and weeds.
However, using deep learning algorithms on drones poses challenges due to their limited computational capabilities. To address this, the paper proposes a cloud computation approach that enables real-time object detection and video monitoring. The results show that traditional computer vision algorithms are not fast enough for real-time monitoring, and deep learning algorithms are a more suitable alternative. The proposed cloud computation approach provides a feasible solution to overcome the computational limitations of drone systems. This research paper makes a significant contribution to the field of drone systems and real-time object detection by proposing a new approach that can be used in various applications, including vigilance, redeem and save, and agricultural monitoring. The proposed approach can also be extended to other applications that require real-time object detection in limited resource environments.
The history of drones dates back many years and it is possible to classify them on the basis of their flight speed, ability to stabilise position, hovering or loitering capability, environmental conditions as well as other characteristics. Various types of Unmanned Air Vehicles, each having its own.
Key Words: Object-Detection, UAVs, Cloud Tracking, Drone, Region based detection, YOLO, SSD, Traditional computer vision algorithms, Deep learning.
1.INTRODUCTION Computer vision has improved significantly in recent years as a result of the advancement of deep learning algorithms [11], advances in hardware capabilities, and more data availability. Detecting items in a specific category such as people, cars, or animals within an image and reporting the location and extent of each object instance is one of the most commonly studied aspects of computer vision.Object detection, including object
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