Optical multi-band detection of unmanned aerial vehicles with YOLO v4 convolutional neural network

被引：3
|
作者：
Golyak, Igor S. [1 ]
Anfimov, Dmitry R. [1 ]
Fufurin, Igor L. [1 ]
Nazolin, Andrey L. [1 ]
Bashkin, Sergey, V [1 ]
Glushkov, Vladimir L. [1 ]
Morozov, Andrey N. [1 ]
机构：
[1] Bauman Moscow State Tech Univ, Moscow 105005, Russia
来源：
SPIE FUTURE SENSING TECHNOLOGIES (2020) | 2020年 / 11525卷
关键词：
unmanned aerial vehicle; drone detection; convolutional neural network; safety; remote sensing;
D O I：
10.1117/12.2584591
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents a method for optical detection drones using the YOLO v4 neural network. Recognition performs simultaneously in the visible (Vis) and long-wave infrared (LWIR) ranges. The results of UAV detection on various types of urban background environment at day and night conditions, as well as at different distances from cameras, are presented. An algorithm for detecting of unmanned vehicles in the video cameras field of view of the Vis and LWIR ranges is described. This algorithm takes as input the outputs of two neural networks that recognize the drone in two ranges and estimates the probability of detection. Its shown that the YOLO v4 neural network recognizes unmanned objects on various background substrates with a minimum temperature difference of 0.4 degrees on the NEC 2640 thermal imager.
引用
收藏
页数：6
相关论文
共 11 条
  • [1] An iterative up-sampling convolutional neural network for glass curtain crack detection using unmanned aerial vehicles
    Huang, Jiaxi
    Liu, Guixiong
    JOURNAL OF BUILDING ENGINEERING, 2024, 97
  • [2] Vision-Based Multi-detection and Tracking of Vehicles Using the Convolutional Neural Network Model YOLO
    Moaga, Mpho
    Chunling, Tu
    Owolawi, Pius
    INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 2, INTELLISYS 2023, 2024, 823 : 519 - 530
  • [3] Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles
    Ha, Jin Gwan
    Moon, Hyeonjoon
    Kwak, Jin Tae
    Hassan, Syed Ibrahim
    Dang, Minh
    Lee, O. New
    Park, Han Yong
    JOURNAL OF APPLIED REMOTE SENSING, 2017, 11
  • [4] Deep Convolutional Neural Network for Flood Extent Mapping Using Unmanned Aerial Vehicles Data
    Gebrehiwot, Asmamaw
    Hashemi-Beni, Leila
    Thompson, Gary
    Kordjamshidi, Parisa
    Langan, Thomas E.
    SENSORS, 2019, 19 (07)
  • [5] Real-Time Object Detection and Tracking for Unmanned Aerial Vehicles Based on Convolutional Neural Networks
    Yang, Shao-Yu
    Cheng, Hsu-Yung
    Yu, Chih-Chang
    ELECTRONICS, 2023, 12 (24)
  • [6] Unmanned Aerial Vehicles anomaly detection model based on sensor information fusion and hybrid multimodal neural network
    Deng, Hongli
    Lu, Yu
    Yang, Tao
    Liu, Ziyu
    Chen, Jiangchuan
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 132
  • [7] Soil Moisture Retrieval Model Design with Multispectral and Infrared Images from Unmanned Aerial Vehicles Using Convolutional Neural Network
    Seo, Min-Guk
    Shin, Hyo-Sang
    Tsourdos, Antonios
    AGRONOMY-BASEL, 2021, 11 (02):
  • [8] Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection
    Liu, Junfu
    Chen, Keming
    Xu, Guangluan
    Sun, Xian
    Yan, Menglong
    Diao, Wenhui
    Han, Hongzhe
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2020, 17 (01) : 127 - 131
  • [9] Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection
    Mowla, Md. Najmul
    Asadi, Davood
    Masum, Shamsul
    Rabie, Khaled
    IEEE ACCESS, 2025, 13 : 3412 - 3433
  • [10] Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices
    Gerber, Christian
    Chung, Mokdong
    JOURNAL OF INFORMATION PROCESSING SYSTEMS, 2016, 12 (01): : 100 - 108
12