Abstract

Unmanned aerial vehicles (UAVs) flown by adversaries are an emerging asymmetric threat to homeland security and the military. To help address this threat, we developed and tested a computationally efficient UAV detection algorithm consisting of horizon finding, motion feature extraction, blob analysis, and coherence analysis. We compare the performance of this algorithm against two variants, one using the difference image intensity as the motion features and another using higher-order moments. The proposed algorithm and its variants are tested using field test data of a group 3 UAV acquired with a panoramic video camera in the visible spectrum. The performance of the algorithms was evaluated using receiver operating characteristic curves. The results show that the proposed approach had the best performance compared to the two algorithmic variants.

© 2017 Optical Society of America

Unmanned-aerial-vehicle based optical camera communication system using light-diffusing fiber and rolling-shutter image-sensor

Yun-Han Chang, Shang-Yen Tsai, Chi-Wai Chow, Chih-Chun Wang, Deng-Cheng Tsai, Yang Liu, and Chien-Hung Yeh
Opt. Express 31(11) 18670-18679 (2023)

Harbor-border inspection for unmanned aerial vehicle based on visible light source tracking

Renhai Feng, Zhaolin Zhang, Zhimao Lai, Sheng Xie, and Xurui Mao
Appl. Opt. 60(31) 9659-9667 (2021)

Wireless ultraviolet cooperative unmanned aerial vehicle formation communication link maintenance method

Taifei Zhao, Yi Zhao, and Kun Liu
Appl. Opt. 61(24) 7140-7149 (2022)

Using continous wavelet analysis for monitoring wheat yellow rust in different infestation stages based on unmanned aerial vehicle hyperspectral images

Qiong Zheng, Wenjiang Huang, Huichun Ye, Yingying Dong, Yue Shi, and Shuisen Chen
Appl. Opt. 59(26) 8003-8013 (2020)

IR radiation characteristics and operating range research for a quad-rotor unmanned aircraft vehicle

Mali Gong, Rui Guo, Sifeng He, and Wei Wang
Appl. Opt. 55(31) 8757-8762 (2016)