## 2 LSDF-Net and Structural Optimization of the Network
### False-Alarm-Object Learning and Multi-Anchor Assignment Strategy  
Infrared dim small target detection in complex sky cloud background scenarios is a challenging problem [33]. To reduce the false alarms of the network, we analyzed the sources of false alarms and found that the interference mainly comes from cloud edges, blind and flicking pixels, and system random noise, as shown in Table 1.  
Based on this, a false-alarm-object learning strategy is adopted, in which the detection network learns infrared dim small targets and other interfering objects including cloud edges, blind and flicking pixels, and system random noise. The images of the multiple objects are shown in Figure 6. This strategy converts object detection tasks into object classification tasks. Compared to single-type dim small target detection, false-alarm-object detection can help to reduce false alarms. In the task of target detection, one-stage target detection algorithms usually generate a series of anchor boxes on the image, regarding these anchor boxes as potential candidate regions [34]. The model predicts whether these candidate regions contain targets and predicts the category of targets. In addition, since the anchor box position is fixed, it usually cannot coincide with the target bounding box, so the anchor box needs to be adjusted to form a real bounding box that can accurately describe the object position. In infrared dim small target detection, the target usually occupies 1-15 pixels and is usually contained in only one anchor box. In this article, the input image is divided into multiple grids, which are used as anchor boxes to detect the target bounding box. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Objects** & **Pixel Size** & **Energy Concentration** & **Signal Strength** & **Movement** \\ \hline Dim small target & 1–15 & High & Low–high & Stable speed \\ Cloud edges & 5–30 & Medium & Low–high & Slow speed \\ Blind and flicking pixels & 1–2 & High & Random & Fixed position \\ System random noise & 8–12 & High in vertical direction & Random & Random position \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on the detected objects. Figure 6: Diagram of the detected objects: (**a**) dim small target; (**b**) cloud edges; (**c**) blind and flicking pixels; (**d**) system random noise. Each real bounding box contains seven values, and its output is  
\[output=[x,y,w,h,c1,c2,conf] \tag{6}\]  
where output represents the output value of the bounding box; x, y is the target center coordinate; w, h represents the width and height of the target; c1 represents the target category label; c2 represents the cloud edges, blind and flicking elements, and system random noise label; and conf represents the target confidence level. However, for single-anchor box assignment, when the target center is close to the middle of the anchor boxes, the target label will be assigned to the closest anchor box, but the location of the target center might swing between the nearby anchor boxes due to the signal noise and target movement, which may lead to a decrease in the detection rate of the target. Especially for moving targets crossing the boundary of anchor boxes, the switching of anchor boxes may decrease the continuity of target detection. To reduce the missed detections of the network, this article adopts a multi-anchor box assignment strategy. A buffer area is set, as shown in the dashed area in Figure 7. When the target is located in the buffer area, it is considered to belong to both anchor boxes, and the target labels are assigned to the anchor boxes. As shown in Figure 7, Target 1 is inside the anchor frame (Anchor1) that performs the target detection; Target 2 is in the middle of two adjacent anchor frames (Anchor1 and Anchor3), and both anchor frames are assigned target labels and perform the target detection together; and Target 3 is in the middle of four anchor frames (Anchor1, Anchor2, Anchor3, and Anchor4), which are assigned target labels and also perform the target detection together.