A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition

Abstract

The use of drones in various applications has now increased, and their popularity among the general public has increased. As a result, the possibility of their misuse and their unauthorized intrusion into important places such as airports and power plants are increasing, threatening public safety. For this reason, accurate and rapid recognition of their types is very important to prevent their misuse and the security problems caused by unauthorized access to them. Performing this operation in visible images is always associated with challenges, such as the small size of the drone, confusion with birds, the presence of hidden areas, and crowded backgrounds. In this paper, a novel and accurate technique with a change in the YOLOv4 network is presented to recognize four types of drones (multirotors, fixed-wing, helicopters, and VTOLs) and to distinguish them from birds using a set of 26,000 visible images. In this network, more precise and detailed semantic features were extracted by changing the number of convolutional layers. The performance of the basic YOLOv4 network was also evaluated on the same dataset, and the proposed model performed better than the basic network in solving the challenges. Compared to the basic YOLOv4 network, the proposed model provides better performance in solving challenges. Additionally, it can perform automated vision-based recognition with a loss of 0.58 in the training phase and 83% F1-score, 83% accuracy, 83% mean Average Precision (mAP), and 84% Intersection over Union (IoU) in the testing phase. These results represent a slight improvement of 4% in these evaluation criteria over the YOLOv4 basic model.

TransLearn-YOLOx: Improved-YOLO with Transfer Learning for Fast and Accurate Multiclass UAV Detection

“…II. It is evident that the mAP of YOLOv5 and v7 with transfer learning approach has outperformed the work given in [13], [10], [14], [15] and [16] with reduced amount of training. The TransLearn-YOLOv7 also performed well in terms of F1 score and yielded the highest value compared to both the YOLOv4 and YOLOv5 existing schemes.…”

“…The detection rate and average accuracy showed performance improvement, but it still needs additional data in complex weather conditions for further improvements [15]. The neural network was trained, tested, and evaluated using datasets containing different kinds of UAVs (multirotor, fixed-wing aircraft, helicopters, and vertical takeoff landing aircraft) and birds and achieved an 83% mAP [16].…”

“…This dataset was then classified using YOLOv2. Darknet was used as the YOLOv4 backbone for UAV vs. bird detection with an accuracy of 98.3% [17]. One-stage detection-based YOLOv5 has excellent detection accuracy and rapid inference time, but its accuracy rapidly drops when dealing with smaller objects in high-resolution photos.…”

Automatic Road Crack Recognition Based on Deep Learning Networks From Uav Imagery

“…where TP = True Positive FP = False Negative TP (True Positive) means that the input is predicted to be positive and is actually positive, and FP (False Positive) means that the input is predicted to be positive and is not actually positive. TN (True Negative) means that the input is predicted to be negative and is actually negative, and FN (False Negative) means that the input is predicted to be negative and is actually positive (Dadrass Javan et al, 2022). In addition, other evaluation metrics are defined as follows (Simonyan et al, 2014):…”

Hostile UAV Detection and Neutralization Using a UAV System

“…To overcome UAV-related challenges, relying only on control and management measures is not enough; effective means of detection and neutralization are necessary. There are many means of UAV detection and neutralization There are four main technologies of detection: optical in various bands [8,9]; passive acoustic [10]; passive radio-receiving emitted radio radiation from the UAV [11]; and active radio-using radars [12]. The possibilities for neutralizing the detected drones are examined in [6,13,14].…”