An Image-Based Classification Module for Data Fusion Anti-drone System

Jajaga, Edmond; Rushiti, Veton; Ramadani, Blerant; Pavleski, Daniel; Cantelli-Forti, Alessandro; Stojkoska, Biljana Risteska; Petrovska, Olivera

doi:10.1007/978-3-031-13324-4_36

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To recognize and classify UAVs, in our project work described in [28,29], we have investigated differentiating drones from birds in a YOLO v4 platform. In this paper, we propose to use the GoogleNet convolutional neural network in the Optical-Electronic Channel Data Fusion.…”

Section: The Study Materials and Methodsmentioning

confidence: 99%

Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

Semenyuk

Kurmashev

Lupidi³

et al. 2023

EEJET

View full text Add to dashboard Cite

This work reports a study into the possibility of using the GoogleNet neural network in the optoelectronic channel of the Data Fusion system. The search for the most accurate algorithms for detecting and recognizing unmanned aerial vehicles (UAVs) in Data Fusion systems has been carried out. The data processing scheme was selected (merging SVF state vectors and merging MF measurements), as well as the sensors and recognition models on each channel of the system. The Data Fusion model based on the Kalman Filter was chosen, integrating radar and optoelectronic channels. Mini-radars LPI-FMCW were used as a radar channel. Evaluation of the effectiveness of the selected Data Fusion channel model in UAV detection is based on the recognition accuracy. The main study is aimed at determining the possibility of using the GoogleNet neural network in the optoelectronic channel for UAV recognition under conditions of different range classes. The neural network for the recognition of drones was developed using transfer training technology. For training, validation, and testing of the GoogleNet neural network, a database has been built, and a special application has been developed in the MATLAB environment. The capabilities of the developed neural network were studied for 5 variants of the distance to the object. The detection objects were the Inspire 2, DJI Phantom 4 Pro, DJI F450, DU 1911 UAVs, not included in the training database. The UAV recognition accuracy by the neural network was 98.13 % at a distance of up to 5 m, 94.65 % at a distance of up to 20 m, 92.47 % at a distance of up to 20 m, 90.28 % at a distance of up to 100 m, and 88.76 % at a distance of up to 200 m. The average speed of UAV recognition by this method was 0.81 s.

show abstract

Section: The Study Materials and Methodsmentioning

confidence: 99%

Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

Semenyuk

Kurmashev

Lupidi³

et al. 2023

EEJET

View full text Add to dashboard Cite

show abstract

Study of the Possibility to Combine Deep Learning Neural Networks for Recognition of Unmanned Aerial Vehicles in Optoelectronic Surveillance Channels

Semenyuk,

Kurmashev,

Alyoshin

et al. 2024

Modelling

View full text Add to dashboard Cite

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Faster RT-DETR in order to identify the average accuracy of UAV recognition. A dataset in the form of images of two classes of objects, UAVs, and birds, was prepared in advance. The total number of images, including augmentation, amounted to 6337. The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. Inference testing was conducted using six videos with UAV flights. On all test videos, RT-DETR-R50 was more accurate by an average of 18.7% in terms of average classification accuracy (Pc). In terms of operating speed, YOLOv5 was 3.4 ms more efficient. It has been established that the use of RT-DETR as the only module for UAV classification in optical-electronic detection channels is not effective due to the large volumes of calculations, which is due to the relatively large number of parameters. Based on the obtained results, an algorithm for combining two neural networks is proposed, which allows for increasing the accuracy of UAV and bird classification without significant losses in speed.

show abstract

An Image-Based Classification Module for Data Fusion Anti-drone System

Cited by 2 publications

References 14 publications

Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

Developing the GoogleNet neural network for the detection and recognition of unmanned aerial vehicles in the data Fusion System

Study of the Possibility to Combine Deep Learning Neural Networks for Recognition of Unmanned Aerial Vehicles in Optoelectronic Surveillance Channels

Contact Info

Product

Resources

About