Article
UAV Detection with Transfer Learning from Simulated Data of
Laser Active Imaging
Shao Zhang
1,2
, Guoqing Yang
3
, Tao Sun
1
, Kunyang Du
1,2
and Jin Guo
1,
*
Citation: Zhang, S.; Yang, G.; Sun, T.;
Du, K.; Guo, J. UAV Detection with
Transfer Learning from Simulated
Data of Laser Active Imaging. Appl.
Sci. 2021, 11, 5182. https://
doi.org/10.3390/app11115182
Academic Editor: João Carlos de
Oliveira Matias
Received: 30 April 2021
Accepted: 27 May 2021
Published: 2 June 2021
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4.0/).
1
State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and
Physics, Chinese Academy of Sciences, Changchun 130033, China; ciompzs@163.com (S.Z.);
suntao@ciomp.ac.cn (T.S.); dukunyang1995@163.com (K.D.)
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Visual Computing Research Center, Shenzhen University, Shenzhen 518061, China; yanggq@szu.edu.cn
* Correspondence: ciompgj@sina.com
Abstract:
With the development of our society, unmanned aerial vehicles (UAVs) appear more
frequently in people’s daily lives, which could become a threat to public security and privacy,
especially at night. At the same time, laser active imaging is an important detection method for night
vision. In this paper, we implement a UAV detection model for our laser active imaging system based
on deep learning and a simulated dataset that we constructed. Firstly, the model is pre-trained on
the largest available dataset. Then, it is transferred to a simulated dataset to learn about the UAV
features. Finally, the trained model is tested on real laser active imaging data. The experimental
results show that the performance of the proposed method is greatly improved compared to the
model not trained on the simulated dataset, which verifies the transferability of features learned
from the simulated data, the effectiveness of the proposed simulation method, and the feasibility
of our solution for UAV detection in the laser active imaging domain. Furthermore, a comparative
experiment with the previous method is carried out. The results show that our model can achieve
high-precision, real-time detection at 104.1 frames per second (FPS).
Keywords: laser active imaging; object detection; transfer learning
1. Introduction
With the increasing demand for high-precision data collection, commercial quadrotor
UAVs have emerged. As ideal platforms of data acquisition, UAVs have high maneuver-
ability even in complex environments. UAVs play a significant role in a variety of fields
such as photography, disaster monitoring, and traffic guidance nowadays [
1
,
2
]. Moreover,
UAVs can also be deployed in a lot of military applications [
3
]. Due to the lack of effective
regulation of UAVs, their abuse poses a threat to the privacy of citizens and the flight
safety in specific places, such as airports. What’s more, the lack of regulation gives rise to
smuggling, terrorist attacks, and other illegal activities. To build a system of regulation, it is
crucial to carry out research on the detection of UAVs. However, this is not an easy task due
to the small size of the UAVs and the limited field of view (FOV) of the detection system,
especially since it would become more difficult when there is no proper illumination.
The laser is a promising source to compensate for the situation of low illumination,
due to its high intensity and high collimation. Therefore, many laser active imaging systems
have been proposed for long-range target identification at night [
4
]. Gating technology is
usually adopted in these systems to mitigate the scattering effects of obscurants and reduce
the influence of background clutter. Researchers have proposed a common implementation
of laser active imaging systems using a long-wave infrared camera with a large FOV as the
detection device to search for the target. The distance of the target is determined by the
time of flight (ToF) of the laser for range gating. At last, a laser range-gated short wave
infrared camera is used for target identification [
5
]. In 2015 [
6
], this author proposed a target
Appl. Sci. 2021, 11, 5182. https://doi.org/10.3390/app11115182 https://www.mdpi.com/journal/applsci
