# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 4 Methodology
### Dataset  
There are two datasets used in the experiments, which are described below.  
1. Simulated dataset: The dataset consists of simulated laser active illumination images according to the method described in Section 3. Firstly, 744 natural images of UAVs in different scenes are collected by a camera. Then ten simulated images with different  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Filters** & **Size** & **Output** \\ \hline \multicolumn{4}{c}{Backbone} \\ \hline Focus & 32 & \(3\times 3\) & 320 \(\times\) 320 \\ Convolutional & 64 & \(3\times 3\) & 160 \(\times\) 160 \\ BottleneckCSP & 64 & \(1\times 1+3\times 3\) & 160 \(\times\) 160 \\ Convolutional & 128 & \(3\times 3\) & 80 \(\times\) 80 \\ BottleneckCSP & 3 \(\times\) & 128 & \(1\times 1+3\times 3\) & 80 \(\times\) 80 \\ Convolutional & 256 & \(3\times 3\) & 40 \(\times\) 40 \\ BottleneckCSP & 3 \(\times\) & 256 & \(1\times 1+3\times 3\) & 40 \(\times\) 40 \\ Convolutional & 512 & \(3\times 3\) & 20 \(\times\) 20 \\ SPP & & & 20 \(\times\) 20 \\ BottleneckCSP & 512 & \(1\times 1+3\times 3\) & 20 \(\times\) 20 \\ \hline \multicolumn{4}{c}{Head} \\ \hline Convolutional & 512 & \(1\times 1\) & 20 \(\times\) 20 \\ Upsample & & \(2\times 2\) & 40 \(\times\) 40 \\ Concatenation & & & 40 \(\times\) 40 \\ BottleneckCSP & 256 & \(1\times 1+3\times 3\) & 40 \(\times\) 40 \\ Convolutional & 256 & \(1\times 1\) & 40 \(\times\) 40 \\ Upsample & & \(2\times 2\) & 80 \(\times\) 80 \\ Concatenation & & & 80 \(\times\) 80 \\ BottleneckCSP & 128 & \(1\times 1+3\times 3\) & 80 \(\times\) 80 \\ Convolutional & 128 & \(3\times 3\) & 40 \(\times\) 40 \\ Concatenation & & & 40 \(\times\) 40 \\ BottleneckCSP & 256 & \(1\times 1+3\times 3\) & 40 \(\times\) 40 \\ Convolutional & 256 & \(3\times 3\) & 20 \(\times\) 20 \\ Concatenation & & & 20 \(\times\) 20 \\ BottleneckCSP & 512 & \(1\times 1+3\times 3\) & 20 \(\times\) 20 \\ Detection & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architecture of the YOLOv5s model [22].  
illumination centers (\(x_{c}\), \(y_{c}\)) and spot sizes \(w(z)\) are generated from each image under coherent imaging and incoherent imaging, respectively. The selection of illumination center and spot size is random following the constraint that the illumination area covers the UAV target and does not exceed the image edge.
2. Real dataset: We first construct our laser active imaging system according to Figure 2. We choose a continuous laser as the illumination source. The laser beam is collimated and expanded by the transmitting lens and then illuminates the target. The callback signal is acquired in an intensified CCD camera after passing through the collection lens. The detail parameters of the camera and laser are listed in Table 2. The setup and experimental scene are shown in Figure 5 left and right respectively. The transmitting and receiving equipment are placed on a turntable to facilitate scene scanning and subsequent tracking and monitoring. We collect three laser active imaging videos of UAVs using this system in different scenes including city, forest, and sky. The distance between UAV and imaging system is 100-500 m. Then we extracted 861 images from the videos to make up the real dataset.