Drone Detection Using Image Processing Based on Deep Learning (original) (raw)
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Drone Detection Using Deep Learning
EPRA International Journal of Multidisciplinary Research (IJMR)
Drones have widespread application in real life and the industry is expanding rapidly. As they are growing increasingly it is more accessible to the public at cheaper prices. They are used for espionage and can be converted into gruesome weapons. Hence it is very important to monitor and detect unauthorized drones entering into the restricted regions in order to maintain peace and prevent chaos. The Technology stack implied here is You Only Look Once (YOLO v5) which is a real time object detection system. In recent times Yolo is a profound algorithm used for real time object or image detection . The Yolo trained model is trained with pictures of drones and birds so that the trained model can differentiate between and prevent false prediction of drones . Everytime a drone is detected in the camera an alert message is sent to the higher officials so that the drone could be eliminated. This algorithm comprises 3 techniques namely: Residual blocks, Bounding box regression and Intersecti...
Aerospace, 2022
Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is...
Drones
Drones have many applications in our daily lives and can be employed for agricultural, military, commercial, disaster relief, research and development, and many other purposes. There has been a significant increase in the usage of small drones/unmanned aerial vehicles in recent years. Consequently, there is a rising potential for small drones to be misused for illegal activities, such as terrorism and drug smuggling. Hence, there is a need for accurate and reliable UAV identification that can be used in various environments. In this paper, different versions of the current state-of-the-art object detection model, i.e., YOLO models, are used, by working on the principles of computer vision and deep learning to detect small UAVs. To improve the accuracy of small UAV detection, this paper proposes the application of various image-processing techniques to the current detection model, which has resulted in a significant performance increase. In this study, a mAP score of 96.7% was obtain...
Drone Detection Using Convolutional Neural Networks
2020
In image processing, it is essential to detect and track air targets, especially UAVs. In this paper, we detect the flying drone using a fisheye camera. In the field of diagnosis and classification of objects, there are always many problems that prevent the development of rapid and significant progress in this area. During the previous decades, a couple of advanced classification methods such as convolutional neural networks and support vector machines have been developed. In this study, the drone was detected using three methods of classification of convolutional neural network (CNN), support vector machine (SVM), and nearest neighbor. The outcomes show that CNN, SVM, and nearest neighbor have total accuracy of 93%, 88%, and 80%, respectively. Compared with other classifiers with the same experimental conditions, the accuracy of the convolutional neural network classifier is satisfactory.
A DEEP LEARNING APPROACH TO CLASSIFY DRONES AND BIRDS
IRJET, 2023
Drones are gaining popularity not just for recreational use, but also for various engineering, disaster management, logistics, and airport security applications. However, their potential use in malicious activities has raised concerns about physical infrastructure security, safety, and surveillance at airports. There have been several reports in recent years of unauthorized drone use causing disruptions in airline operations. To address this problem, a new deep learning-based method has been proposed in this study. This approach is efficient in detecting and recognizing two types of drones and birds, and it outperforms existing detection systems in the literature. The physical and behavioral similarities between drones and birds often lead to confusion, but the proposed method can not only detect the presence or absence of drones but also distinguish between different types of drones and differentiate them from birds.
Fast Object Detection for Quadcopter Drone Using Deep Learning
2018 3rd International Conference on Computer and Communication Systems (ICCCS)
The paper presents our research progress in the development of object detection using deep learning based on drone camera. The grand purpose of our research is to deliver important medical aids for patients in emergency situations. The case can be simplified into delivery of an item from start to the goal position. We will exploit the drone technology for transporting items efficiently. In sending process, our drone must detect the object target, where the items will be delivered. Therefore, we need object detection module that can detect what is in video stream and where the object is by using GPS as well. To implement the module, we use combination of MobileNet and the Single Shot Detector (SSD) framework for fast and efficient deep learning-based method to object detection. The ability of deep learning to detect and localize specific objects is studied by conducting experiments using drone camera and, as comparison, using stereo camera Minoru.
Drone Tracking with Drone using Deep Learning
International Journal of Computer and Information Technology(2279-0764)
With the development of technology, studies in fields such as artificial intelligence, computer vision and deep learning are increasing day by day. In line with these developments, object tracking and object detection studies have spread over wide areas. In this article, a study is presented by simulating two different drones, a leader and a follower drone, accompanied by deep learning algorithms. Within the scope of this study, it is aimed to perform a drone tracking with drone in an autonomous way. Two different approaches are developed and tested in the simulator environment within the scope of drone tracking. The first of these approaches is to enable the leader drone to detect the target drone by using object-tracking algorithms. YOLOv5 deep learning algorithm is preferred for object detection. A data set of approximately 2500 images was created for training the YOLOv5 algorithm. The Yolov5 object detection algorithm, which was trained with the created data set, reached a succe...
Advances in Materials Science and Engineering
During the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats to wild areas and forest management. Traditional firefighting methods, which rely on ground operation inspections, have major limits and may threaten firefighters’ lives. As a result, remote sensing techniques, particularly UAV-based remotely sensed techniques, are currently among the most sought-after wildfire-fighting approaches. Current improvements in drone technology have resulted in significant breakthroughs that allow drones to perform a wide range of more sophisticated jobs. Rescue operations and forest monitoring, for example, demand a large security camera, making the drone a perfect tool for executing intricate responsibilities. Meanwhile, growing movement of the deep learning ...
Detection and Tracking of Drones Using Artificial Intelligence Techniques - a Survey
Zenodo (CERN European Organization for Nuclear Research), 2022
Enormous mechanisms and mediums are being employed to threaten the defense system and civilians. Drone strikes are one of them, which may refer to the unloading of explosions and supervision as such. So, detecting and tracking the Drone could be a viable solution for any organization to tackle the aerial threat challenges and secure the environment from malicious activities. Thus the present research discusses the current paradigm of Drone strikes, challenges and solutions to deal with such security concerns. The present article aims to examine the current status of Drone Detection critically, and the applicability and advancement of Artificial Intelligence enabled technology. The present research article also explores the working mechanism of the Object detection system and Convolutional Neural Network (CNN) on the ground level to help future researchers gain knowledge. The paper also explains the maximum reach of accuracy achieved by the various model and algorithms so that a new benchmark can be defined.
Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
Sensors
Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in d...