# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 4 Experiments
### Result and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1** & **mAP50 (\%)** & **FPS (f/s)** \\ \hline YOLOv5s & 83.3 & 71.7 & 77.1 & 78.6 & 53.8 \\ SSD & 77.8 & 33.2 & 46.5 & 56.6 & 55.2 \\ Faster-RCNN & 80.9 & 46.4 & 58.9 & 77.5 & 29.6 \\ RetinaNet & 67.7 & 79.5 & 73.1 & 65.2 & 33.2 \\ CenterNet & 73.3 & 43.4 & 54.5 & 67.6 & 37.1 \\ FCOS & 84.9 & 76.4 & 80.4 & 81.5 & 36.4 \\ Ours & **87.3** & **86.0** & **86.6** & **85.6** & **57.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental results on the CATHR-DET. effectiveness of our improvements, which can address, to a certain extent, the issues of missed detection and false detection caused by target occlusion and lighting changes in the task of carrier-based aircraft tailhook recognition in complex environments. The partial recognition results of our proposed model on the CATHIR-DET are shown in Figure 11. The first two rows show samples that are correctly recognized in real landing scenarios and simulated scenarios, respectively. The last row shows correctly detected negative samples, false detections, and missed detections. The seventh image is a correctly detected negative sample, classified as the tailhook not being lowered. The eighth image shows a false detection caused by the leg of the commanding officer resembling the tailhook in shape. The ninth image shows a missed detection due to the texture color matching the body of the aircraft and the excessive upward viewing angle of the tailhook causing color variation. The recognition results of various methods on the VOC dataset are shown in Figure 12. It can be observed that each algorithm can identify targets with obvious features and large proportions in the image. For the keyboard in the first image, due to the repetitive texture causing the features to be insufficiently obvious, both SSD and FCOS failed to detect it. For the person on the sailboat in the second image, limited by the fixed anchor box size and regression method, SSD and Faster-RCNN had some missed detections. For the handbag in the third image, only the proposed method and CenterNet recognized it, while the other three methods failed to detect this target. For the group of donuts in the fourth image, SSD and Faster-RCNN had a large number of missed detections, and FCOS missed some heavily occluded objects such as cups. Therefore, our method significantly outperforms the other four methods, accurately perceiving and recognizing small and occluded targets and greatly reducing the occurrence of missed detections. Figure 11: Recognition samples on the CATHR-DET. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1** & **mAP50 (\%)** \\ \hline YOLOv5s & 72.4 & 66.2 & 69.1 & 71.7 \\ SSD & 66.3 & 38.4 & 48.6 & 69.7 \\ Faster-RCNN & 75.8 & 53.7 & 62.8 & 65.7 \\ RetinaNet & 71.9 & 60.5 & 65.7 & 71.4 \\ CenterNet & 65.8 & 33.9 & 44.7 & 66.4 \\ FCOS & 77.6 & 70.3 & 73.7 & 76.3 \\ Ours & **80.9** & **77.8** & **79.3** & **81.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of experimental results on the VOC dataset.