## 4 Experimental Results
### Hub Object Detection  
In this section, we intend to examine the result of applying the \(SSD7\) algorithm to find the hub object in an input image. For the learning process, the bounding box ground truth information for all images should be created and applied to the algorithm. To handle that, we use the _labelImg_ software [64] to label our image dataset. For this purpose, first we created a data set including 10,000 images; then, for convenience, we randomly sub-sampled it to have just 1000 images. Then, we augmented all these images by labelImg to feed bounding box information to SSD7. Table 2 shows the results of the implementation of the SSD7 algorithm on this reduced dataset. In this experiment, in order to reduce the time overhead, the dimensions of the input images from the data are reduced to the values \((300,480)\), then the algorithm is run for _confidence threshold_ = 0.3 and _Intersection Over Union threshold_ (**IoU**) = 0.5. In the literature, there are some measurement metrics such as _True Positive (TP)_, _True Negative (TN)_, _False negative (FN)_, and _False Positive (FP)_ that are widely used for object detection problems [65; 66]. \(TP\) indicates the total number of samples that are classified correctly. \(FN\) means the number of samples belonging to the class set but incorrectly classified. TN represents the number of correctly classified samples not belonging to the class set, and \(FP\) refers to the number of unclassified samples that have been misclassified. Based on defined parameters, accuracy can be calculated using Equation(15) [66]:  
\[Accuracy=(TP+TN)/(TP+TN+FP+FN) \tag{15}\]  
In Table 2, the results for _Accuracy_ **(ACC)**, and _confidence mean_ (**Conf** or **mAP**_(mean Average Precision)_) are obtained in a state where the _Non-Max Suppression (NMS)_[67] is also implemented. Now, to determine the best configuration, we change the _epoch_ value from 20 to 600. For \(epoch=20\), we have some false negatives and a lot of false positives, while it increases to 60, the FPs are significantly reduced, but the \(FN\) is very high. While the \(epoch\) is equal to 200, we have no \(FN\) with a few \(FPs\). After that, with the \(epoch\) increement from 200 to 600, the results are not improved (the validation loss and training loss for \(epoch=600\) are shown in Figure 8). As we can see in Table 2, the best accuracy with the highest confidence mean is obtained for the \(epoch=200\) where \(ACC=99.5\%\) and \(Conf=0.6699\) are satisfied. Hereafter, we consider the \(epoch\) value to be 200. Now, the impacts of two important parameters including \(confidence\)_thresh_ and _IoU_ on accuracy should be evaluated. For this purpose, these two parameters are changed according to Table 3, and at each stage, the run time or _Prediction Time per Image_ (**PTPI**) is calculated with \(ACC\). Obtained results show that for a high confidence level, the \(FN\) value will be very high (\(FN=156\) and \(160\) for _confidence thresh_ = 0.7 with different IoUs). For the \(confidence\)_thresh = 0.7, the \(ACC\) is obtained at 22% and 20% when \(IoU~{}=~{}0.50\) and 0.35, respectively. In comparison, for \(confidence\)_thresh_ = 0.3, the accuracy is increased sharply to _99.5\(\%\)_. Further reduction of the \(confidence\)_thresh_ from 0.3 to 0.22 can lead to an increment on \(FP\) and consequently a decrease of \(ACC\). In addition, the average run time for hub detection is estimated at about 0.01594 s. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epochs** & **Val loss** & **ACC (\%)** & **Conf (mAP)** \\ \hline
20 & 3.5099 & 49.5 & 0.5119 \\
60 & 2.5054 & 82.5 & 0.4949 \\
200 & 1.9740 & 99.5 & 0.6699 \\
400 & 2.1127 & 97.1 & 0.5368 \\
600 & 2.2741 & 95.5 & 0.5170 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of proposed hub object detection based on SSD7. Input image: [\((h,w)=(300,480)\)]. In Figure 9, the effect of the changes of two parameters on \(ACC\) is well illustrated.