## 4 Analysis of Experimental Results
### Precise Identification Technology for Emergency Airdrop Stations
#### 4.2.5 Visual Analysis  
In this paper, target detection of emergency airdrop stations using the APCR-YOLOv8 algorithm was implemented to understand the model before and after improvement for the task. The before-and-after comparison is demonstrated by heatmap visualization, as shown in Figure 13. Figure 13a is the original input image, Figure 13b is the target-detection heatmap of YOLOv8, and Figure 13c is the target-detection heatmap of APCR-YOLOv8. The heat map uses a gradient of colors to indicate the spatial distribution and recognition priority of potential targets. Hotter regions (red to yellow) are where targets will likely be present in the algorithm's predictions, while cooler regions (blue to purple) indicate less attention. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **P\%** & **R\%** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]-0.95/\%** & **Params/M** & **FLOPs/G** & **FPS** \\ \hline Faster R-CNN & – & – & 80.69 & – & 136.69 & 369.7 & 38.1 \\ SSD & – & – & 60.70 & – & 23.13 & 60.8 & 182.3 \\ YOLOv3-tiny & 73.5 & 70.7 & 77.6 & 47.2 & 8.67 & 12.9 & **474.3** \\ YOLOv5n & 64.6 & 68.6 & 66.0 & 42.6 & 1.76 & 4.1 & 199.7 \\ YOLOv6n & 82.3 & 71.4 & 0.78 & 56.5 & 4.30 & 11.1 & 203.8 \\ YOLOv7-tiny & 72.6 & 58.9 & 73.3 & 45.5 & 6.01 & 13.1 & 201.3 \\ YOLOv8n & 89.1 & 76.4 & 88.5 & 64.8 & 3.00 & 8.1 & 188.4 \\
**OURS** & **90.8** & **85.5** & **92.4** & **68.2** & **1.67** & **5.3** & 151.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison results of multiple testing models. As can be seen from Figure 13, there are the following differences before and after the model improvement. (1) Concentration of the heat map: In the heat map of YOLOv8, the hotspots are more scattered, which indicates that the algorithm's identification of the potential target location is carried out in the broader area, not specifically targeting a specific target. On the other hand, the heat map of APCR-YOLOv8 shows more concentrated hotspot areas, indicating that the improved model algorithm is more accurate and focused on target localization. (2) Clarity of the heat map: the edges of the hotspot areas in the APCR-YOLOv8 heat map are more precise and sharper than in the YOLOv8 heat map. This means that the algorithm has improved the recognition of the target boundary, reducing the background interference and improving the accuracy of target detection. (3) Difference in heat level: compared to YOLOv8, the heat map of APCR-YOLOv8 shows more excellent contrast in color intensity, especially in the target area. This indicates that the algorithm has more confidence in the target, reinforcing the contrast between the target area and the surroundings. In summary, the heat map performance of the APCR-YOLOv8 algorithm in the target detection task is significantly improved compared to the original YOLOv8 algorithm. Specifically, APCR-YOLOv8 performs better in critical metrics such as clarity, concentration, and confidence. To reflect the performance of the APCR-YOLOv8 algorithm, it was qualitatively analyzed with the YOLOv8 algorithm in this paper. Examples of the detection results of the two detection networks in the same test environment are shown in Figure 14.  
Figure 13: Comparison of YOLOv8 and APCR-YOLOv8 heat maps. (a) Original plot of the dataset; (b) heat map of YOLOv8; (c) heat map of APCR-YOLOv8. Figure 14: Example analyses of emergency airdrop station detection using APCR-YOLOv8. (**a**) Solving the false detection problem by accurately distinguishing between actual and false targets; (**b**) Solving the tiny target missed detection problem by enhancing the detection of small and difficult-to-detect targets; (**c**) Solving the repeat detection problem by avoiding repeated detections of the same target; (**d**) Enhancing detection confidence with higher confidence scores in the bounding boxes. As shown in Figure 14, the APCR-YOLOv8 algorithm demonstrates superior performance over YOLOv8 in several aspects.