# Tailhook Recognition for Carrier-Based Aircraft Based on YOLO with Bi-Level Routing Attention
## 4 Experiments
### Result and Analysis  
The experimental results of the proposed method, the baseline model, and other classic target detection algorithms on the self-constructed tailhook dataset, CATHR-DET, are compared in Table 4. It is evident that our method outperforms others in the five metrics, indicating that it not only identifies targets with high accuracy but also effectively covers targets of different sizes and shapes, demonstrating good detection comprehensiveness and robustness. In more detail, it is 6.2 percentage points higher than the suboptimal method FCOS under the F1 metric, and, at the same time, it is 7.0 percentage points higher than YOLOv5s under the mAP50 metric, indicating that it has more refined and comprehensive feature extraction and recognition capabilities in handling object-detection tasks. In terms of frame rate, the relatively low complexity of the GLoss loss function for bounding box regression enhances localization accuracy while accelerating the model's regression speed, reducing computational burden, and improving the frame rate, enabling faster image processing. Our method achieves a processing speed of 57.1 frames per second (fps). A frame rate of 30 frames per second is a common standard for real-time video processing, meaning the system needs to process and recognize 30 consecutive images per second. This demonstrates that our method is highly suitable for real-time video processing. Given our method's high frame rate, it can effectively handle real-time carrier-based aircraft landing videos, ensuring the timely recognition of tailhooks without noticeable delay. Furthermore, the proposed D_C3 module and the adjustment of loss function weights in this paper effectively address the issue of insufficient detection capability for small targets in the original model, increasing the Recall rate by 15.3 percentage points compared to YOLOv5s, thereby alleviating the problem of missed detections. Due to the fixed size and proportion of the anchor box design of the SSD, it is not sensitive to small targets in specific scenarios, and its single output layer limits its ability to capture complex or occluded targets, resulting in poor performance in the Recall rate. RetinaNet adopts focal loss to address the issue of imbalance between positive and negative samples, but it is not sensitive enough to the recognition of specific classes in the tailhook recognition scenario, leading to lower overall accuracy. FCOS performs comparably to Faster-RCNN in terms of mAP50 but significantly outperforms Faster-RCNN in Recall rate. This may be because FCOS directly performs object localization and classification on the feature map, enabling it to better adapt to the detection of small objects in scenarios; meanwhile, as a single-stage detector, it optimizes the object-detection task in an end-to-end manner, reducing model complexity, which also contributes to the improvement of the Recall rate. To further validate the efficacy of the proposed method, we compared it with other classic object-detection algorithms using the VOC dataset. The outcomes of these experiments are detailed in Table 5. It can be observed that compared with the experimental results of YOLOv5s, SSD, Faster-RCNN, RetinaNet, CenterNet, and FCOS on the VOC dataset, our method achieves the optimal results in all metrics. Compared with the second-best algorithm, our method improved Precision by 3.3%, Recall by 7.5%, and F1 score by 5.6%. Moreover, when benchmarked against the original YOLOv5s model, our approach achieved a 9.7% elevation in mean average precision (mAP). This result demonstrates the