## 4 Preprocessing Module
### Detection and Recognition  
The identity and location information of the executors and participants of the KMNs are prerequisites for the completion of the collection of KMNs. A sub-module is thus required to accurately and in real-time detect and identify targets in specific areas of the airfield. The proposed framework utilizes YOLOv5 as the basic framework for GSE and aircraft detection and classification. YOLOv5 uses the architecture of Darknet53 with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **No. 734** & **No. 939** & **Total** \\ \hline In-block & 14 & 2 & 16 \\ Off-block & 13 & 3 & 16 \\ Docking of mobile aircraft landing stairs & 3 & 4 & 7 \\ Undocking of mobile aircraft landing stairs & 2 & 1 & 3 \\ Total & 32 & 10 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of video sequences in the dataset.  
Figure 4: Two remote stands at Beijing Capital International Airport (IATA: PEK, ICAO: ZBAA) where original videos of the dataset were captured.  
Figure 5: Flowchart of the preprocessing module.  
the Focus Structure and Cross Stage Partial Network as the backbone, PANet and SPP block as the neck, and the head containing three branches corresponding to outputs with three different scales [31]. The loss function of YOLOv5 consists of three parts and can be expressed as follows:  
\[L=L_{cls}\ +L_{obj}\ +L_{reg} \tag{1}\]  
where the classification loss function \(L_{cls}\) is a cross-entropy loss function and used for classification and recognition, the confidence loss function \(L_{obj}\) is a binary cross entropy function used to determine whether an object exists within a projected bounding box, and the regression loss function \(L_{reg}\) is used to predict the bounding box regression. Considering that \(L_{reg}\) has a large impact on the positioning accuracy of the target, we compared four different methods for \(L_{reg}\) calculation: IoU-loss \(L_{\text{IoU}}\), Distance IoU-loss (DIoU-loss) \(L_{\text{DIoU}}\), Generalized IoU-loss (GIoU-loss) \(L_{\text{GIoU}}\), and Complete IoU-loss (CIoU-loss) \(L_{\text{CIoU}}\).  
\[\begin{split} IoU&=\frac{\text{B}\cap\text{B} \cap\text{B}^{gt}}{\text{B}\cup\text{B}^{gt}}\\ GloU&=1-IoU+\frac{\left|\text{E}-\text{B}\cup \text{B}^{gt}\right|}{\left|\text{E}\right|}\\ DIoU&=1-IoU+\frac{\left(\text{x}_{c}-\text{x}_{c}^ {gt}\right)^{2}+\left(\text{y}_{c}-\text{y}_{c}^{gt}\right)^{2}}{L^{2}}\\ CloU&=DIoU+\frac{v^{2}}{\left(1-IoU\right)+v} \end{split}, \tag{2}\]  
where \(\left(x_{c},y_{c}\right)\) and \(\left(x_{c}^{gt},y_{c}^{gt}\right)\) are the coordinates of the center points of the detected bounding box B and corresponding ground truth \(\text{B}^{gt}\), respectively, E is the smallest enclosing box covering the detected bounding box and ground truth, \(L\) is the diagonal length of the E, and \(v\) is defined as [29],  
\[v=\frac{4}{\pi^{2}}\left(\arctan\frac{w^{gt}}{h^{gt}}-\arctan\frac{w}{h}\right) ^{2}, \tag{3}\]  
where \(w^{gt}\) and \(h^{gt}\) are the width and height of \(\text{B}^{gt}\), respectively, and \(w\) and \(h\) are the width and height of B, respectively. Through the comparative experiment (related results are shown in Table 4), CIoU-loss is used in the proposed framework to improve the positioning accuracy of the object. The reason for choosing CIoU-loss as the bounding box regression loss is that it takes into account two geometrical factors: the distance between the central points of bounding boxes and the aspect ratio of the bounding box.