## 4 Experiments
### Experiment and Analysis  
#### 4.2.1 Object Detection  
In the evaluation of object detection performance, this paper shifts from the commonly used mean average precision (mAP@50) and mAP@50-95 metrics to mAP@80 and mAP@75-95 at higher confidence thresholds as the primary evaluation metrics. The reason for this change is that the core goal of this method is to accurately localize the target and calculate pixel displacement, which is then used to infer the real-world speed. Therefore, rather than focusing on the generalization of detection results, the ability to precisely localize the target has a greater influence on the subsequent speed calculation. Relying solely on the lower-threshold mAP@50 might obscure issues related to the inaccurate prediction of boundary boxes, which could lead to misalignments between the detection box and the actual target, thus introducing significant errors during the speed estimation phase. In addition, we found that mAP@80 shows a stronger Pearson correlation (R = 0.91) with velocity estimation accuracy compared to mAP@50 (R = 0.67). This indicates that, from a statistical perspective, using mAP@80 and mAP@75-95 provides a more reliable reflection of the model's actual effectiveness in this task scenario. By contrast, using stricter mAP@80 and mAP@75-95 provides a more accurate reflection of the detection head's ability to precisely capture target positions, thereby enhancing the stability and accuracy of speed measurement. To validate the effectiveness of the proposed YOLOv11-Dd detection head, we compared it with the current mainstream models YOLOv5, YOLOv8, and the base version YOLOv11. Performance evaluation experiments were conducted on a unified test set, with results including mAP@80, mAP@75-95, fps (frame rate), and model parameter count (Params/e6), as shown in Table 3. A comparison of detection results between YOLOv11 and YOLOv11-Dd is presented in Figure 6.  
As shown in Table 3, the proposed improved model YOLOv11-Dd demonstrates outstanding performance in the debris detection task. Specifically, under the mAP@80 metric, it achieves improvements of 3.56%, 3.89%, and 4.65% compared to YOLOv5, YOLOv7, and the original YOLOv11 models, respectively. In the mAP@[75:95] metric, the increases are 1.8%, 0.49%, and 0.79%. This indicates that YOLOv11-Dd offers a significant advantage in detection accuracy under high-confidence requirements. As shown in Figure 6, YOLOv11-Dd shows greater accuracy in the localization of target bounding boxes compared to the original YOLOv11 model. The detection boxes fit the target edges more precisely, enabling a more complete enclosure of the target. This improvement is attributed to the optimization of the feature fusion method in the Concat module of the original YOLOv11 structure. The original model uses a direct feature concatenation approach, which overlooks the feature scale and semantic differences between layers, potentially leading to the loss of critical information. The FA-Concat (Feature Alignment Concat) module introduced in this paper effectively addresses the semantic difference issue in multi-layer feature fusion, enhancing the model's ability to capture key target regions and improving overall detection accuracy. It is important to note that, with the introduction of the residual connection convolution structure, the model complexity of YOLOv11-Dd has increased, leading to a decrease in frame rate (fps) compared to the original YOLOv11 model. However, when compared to the YOLOv8 model, YOLOv11-Dd significantly improves detection accuracy while maintaining a similar parameter count, demonstrating the effectiveness and adaptability of the proposed structure in small-target detection scenarios. In summary, YOLOv11-Dd shows excellent overall performance in high-precision debris detection tasks, making it well-suited for deployment in real-world unmanned drilling machine environments.