Abstract
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 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 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.
Highlights
With the increasing development of drones and their manufacturing technologies, the number of them being used for military, commercial, and security purposes is increasing [1,2,3].In recent years, the use of different types of drones has received much attention due to their efficiency in applications such as airport security, the protection of its facilities, and integration into security and surveillance systems [4,5,6]
This evaluation metric means the degree of overlap between the predicted bounding box and the ground truth bounding box
The proposed convolutional network can overcome a varietyin ofdrone challenges in drone convolutional neural networkneural can overcome a variety of challenges detection and detection and recognition, such helicopters, as multirotors, andbetween distinguishing between recognition, such as multirotors, and helicopters, distinguishing birds and drones birds and drones even As at longer ranges
Summary
With the increasing development of drones and their manufacturing technologies, the number of them being used for military, commercial, and security purposes is increasing [1,2,3].In recent years, the use of different types of drones has received much attention due to their efficiency in applications such as airport security, the protection of its facilities, and integration into security and surveillance systems [4,5,6]. Drones can be considered a serious threat in these security areas, and it is important to develop an efficient approach to detect types of drones in these applications [7,8,9] Such technologies can be used in airport security and any military systems to prevent drone intrusion or to ensure their security [7,10,11]. In this article, based on the physical and behavioral similarities between drones and birds, two types of drones are detected and recognized and their distinction from birds is determined Problems such as the presence ofand drones in crowded environments, varying weather Different Weather Conditions Crowded Differentsuch. Problems as the presence and of drones in crowded environments, varying weather conditions, and different lighting conditions make drone detection difficult (Figure 2).
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