## 4 Experimental Results
### Hub Object Detection
The results indicate that the higher the \(confidence\)\(thresh\), the lower the accuracy while the effect of \(IoU\) variation on accuracy is not very noticeable. Based on achieved results, we set the \(confidence\)\(thresh\) and \(IoU\) values to be 0.3 and 0.50, respectively, in our work. In Figure 10, the results of hub object detection based on the SSD7 algorithm with \(confidence\)\(thresh\) = 0.21 are shown for two sample images from our dataset. In these experiments, for testing, the NMS algorithm is not applied, so several hub objects are identified and located for just an image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Confidence Threshold** & **IoU** & **ACC (\%)** & **PTPI(sec)** & **Conf(mAP)** \\ \hline
0.22 & 0.50 & 99.0 & 0.01450 & 0.5561 \\
0.22 & 0.35 & 99.0 & 0.01316 & 0.5664 \\
0.30 & 0.50 & 99.5 & 0.01500 & 0.6699 \\
0.38 & 0.50 & 89.5 & 0.01599 & 0.5734 \\
0.51 & 0.35 & 64.5 & 0.01453 & 0.6466 \\
0.51 & 0.50 & 67.5 & 0.01479 & 0.6668 \\
0.70 & 0.50 & 22.0 & 0.02250 & 0.7435 \\
0.70 & 0.35 & 20.0 & 0.01710 & 0.7546 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of confidence threshold and Intersection Over Union threshold (IoU) impacts on Accuracy (ACC), Prediction Time per Image (PTPI), and Conf. Input image: \([(h,w)=(300,480)]\). Figure 8: Comparing validation loss and training loss for SSD7 object detection (\(epoch\) = 600). Figure 9: Evaluation of confidence threshold and IoU impacts on the accuracy. In this figure, all objects are shown with their confidence factors, and there are neither \(FN\) nor \(FP\). Results indicate that almost all estimated bounding boxes are well matched to hub positions. In Figure 11, another example is shown where some false positives can be seen for new sample images. False positives can be either due to the low accuracy of the algorithm or the lower threshold for confidence. Therefore, it can be expected that with increasing \(confidence\)_thresh_ or improving accuracy, this problem will be solved. In addition, it is worthy to mention that in our simulation, in most cases, false positives exist along with the true detection. Actually, this means that the main object can easily be discriminated by filtering false estimations. If we add NMS to the algorithm, we can filter all estimated bounding boxes with low confidence scores. For example, in Figure 11a the bearing box bonding with \(confidence=0.52\) and in Figure 11b the bounding box with a \(confidence=0.44\) can accurately extract the actual position of the hub object. Figure 10: Detection of the hub with different accuracies without NMS (Green box: Ground truth box). (a) A near-field sample (b) A far-field sample. Finally, in Figure 12, new scenarios with false negatives are displayed for two low-quality images. In Figure 12a,c, because of the low image quality and also the high amount of \(confidence\)\(thresh\), our proposed object detection algorithm fails to detect the hub. To solve the issue, the threshold value is reduced and adjusted to 0.3. New results in Figure 12b,d clearly indicate that true positions can be estimated even for low-quality images. It is worth mentioning that the results of recent figures are illustrated after the NMS. Figure 11: False positive detection without NMS (\(epoch=10\), \(confidence\)\(thresh=0.20\), \(IoU=0.50\)) (**a**) The first sample (**b**) The second sample.