Abstract of Drone Detection using yolov4 and yolov5
Now days difficult find drone are bird because drones are popular, and increasingly everywhere, so we implemented this project detect the drones.
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, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations and some human cannot find the drone or birds.
This problem, that’s why we implement this project using 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. Furthermore, drones are often confused with birds because of their physical and behavioral similarity.
The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of some visible images containing two types of drones and birds. The proposed deep learning method can directly detect and recognize two types of drones and birds.
Introduction Bird/Drone detection using yolov4 and yolov5
With the increasing development of drones and their manufacturing technologies, the number of them being used for military, commercial, and security purposes is increasing.
The use of different types of drones has received much attention due to their efficiency in applications such as security purpose, the protection of its facilities, and integration into security and surveillance systems.
On the other hand, drones can also be considered a serious threat in these security areas, and therefore, it is important to develop an efficient approach to detect types of drones in these applications. Such technologies can be used in airport security and any military systems to prevent drone intrusion or to ensure their security.
Therefore, the detection, recognition, and identification of countries are crucial in discussing public safety and the threats posed by their existence. Detection is the process of observing the target, and this target may be suspicious and threaten the security of the target environment, recognition is the determination of the target category, and identification refers to diagnosing the type of target category.
System Architecture of drone detection using yolov5 project
H/w and S/W requirements
Hardware:
Computer : System.
Ram : 1GB
Rom : 32GB
Software:
Technology : Machine Learning.
Front End : GUI-tkinter.
IDLE : python 3.10.4