# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 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 maneuverability 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 targetrecognition algorithm that used laser active imaging based on fast contour torque features. However, the algorithm only considers a single background and cannot be extended to complex scenes. All of the studies mentioned above recognize the target in a narrow FOV, but cannot extend to a complex scenario. Our work here focuses on using laser active imaging to perform target detection, including target location and identification, even in a complex scene. There are many traditional hand-crafted, feature-based models, such as histograms of oriented gradients (HOG), scale-invariant feature transform (SIFT), Haar-like and deformable part-based model (DPM) [7], which have achieved good object detection performance in natural images. However, compared with the conventional passive imaging system, laser active imaging produces gray images with higher noise owing to the laser's coherence, which hinders the performance of traditional object detection algorithms. Since the great success of Alexnet [8] in 2012, convolutional neural networks (CNNs) have been widely used in a variety of fields of computer vision, including image classification [9], object detection [10], and image segmentation [11]. CNNs can learn more robust features from data automatically compared with traditional algorithms, in which sophisticated features need to be designed manually. Furthermore, object detection algorithms based on CNN rely on a large amount of annotated data for its training. However, collecting enough data to satisfy the requirements is time-consuming, laborious, and even impossible in many situations. Laser active imaging is a typical situation where few samples can be gathered. Transfer learning is an effective and promising method to alleviate the situation of insufficient data in many domains [12]. Deep transfer learning aims to transfer the prior features extracted from the source domain \(D_{src}\) of a large dataset to the target domain \(D_{tar}\) where data is scarce. [PERSON] et al. [13] achieved a significant improvement in military object recognition by equipping the model with the prior knowledge learned from ImageNet [14].