## 3 Results
### Results of Experiment  
Using the mine-Center-Net algorithm to carry out the test. The foreign body detection process and results are shown in Figure 12.  
Then, the method proposed in this paper is compared with the general algorithm, and the improved method is further analyzed. Finally, each method is evaluated using four indicators: precision, average precision (\(AP\)), mean average precision (MAP) and model inference speed (FPS). MAP is the average of multiple class APs. The specific calculation formula is as follows:  
\[Precision=\frac{Num(TP)}{Num(TP)+Num(FP)}=\frac{Num(TP)}{N} \tag{7}\]  
\(TP\): number of detection boxes with IoU > 0.5. \(FP\): number of detection boxes with IoU \(\leq\) 0.5. \[AP=\frac{\sum\text{Precision}}{Num(Totalobjects)} \tag{8}\]  
Total objects: total number of objects. \[MAP=\frac{\sum AP}{Num(Classes)} \tag{9}\]  
Classes: number of types. In this paper, five groups of control experiments are designed, which are the mine-Center-Net algorithm obtained by expanding the receptive field and improving the loss function and SSD, YOLO V3, Faster R-CNN, and Center-Net. The specific results are shown in the Table 1 below. Anchor-based object detection algorithms, such as YOLO, require the generation of numerous anchor boxes within the foreign object image during the detection process. This inherently increases model redundancy and significantly reduces detection robustness. In contrast, anchor-free object detection, by predicting the center point of the target object directly from the feature map, greatly enhances network efficiency. Furthermore, the absence of anchor boxes eliminates the need for non-maximum suppression (NMS) processing, thereby improving the system's overall detection accuracy. Figure 12: Test results. As demonstrated in the detection results, the proposed mine-Center-Net achieves an average detection accuracy of 91.10% for foreign objects on mining belt conveyors, surpassing the performance of general-purpose algorithms in this specific application. Compared to SSD with VGG16, YOLO V3 with Darknet53, and Faster R-CNN with VGG16, mine-Center-Net exhibits a significant improvement in mean average precision (mAP) of 17.70%, 22.30%, and 13.70%, respectively. In addition to its superior accuracy in identifying various foreign objects, mine-Center-Net achieves a real-time detection speed of 24.05 FPS, representing a speed enhancement of 3.00 FPS, 2.03 FPS, and 3.97 FPS over SSD, YOLO V3, and Faster R-CNN, respectively. The actual detection results are shown in Figure 13.  
In the comparative experiments, the two-stage object detection algorithm used was Faster R-CNN, while the one-stage anchor-based algorithms included SSD and YOLOv3, and the one-stage anchor-free algorithm was Center-Net. The experimental results indicate that among the anchor-based algorithms, Faster R-CNN demonstrated the precision advantages of the two-stage approach, albeit with a slight sacrifice in speed, which aligns with the characteristics of object detection algorithms. The anchor-free model Center-Net, along with the method proposed in this paper, exhibited superiority for the specific task at hand. Furthermore, the proposed method achieved an additional improvement in accuracy after modifications, while the speed remained largely unchanged. From the results, we observed a significant enhancement in the recognition accuracy of the shovel teeth, which are relatively small and prone to being buried. The improved network has enhanced the detection capability for small objects. Our object detection network employs an anchor-free one-stage detection method that achieves end-to-end object detection by predicting center points. Since the predictions