# Intelligent Detection of Parcels Based on Improved Faster R-CNN
## 3 Experiments
### Comparison with Different Methods  
To illustrate the detection performance of the proposed method, we compared our method against 4 state-of-the-art deep learning methods. Their backbone models and loss functions are presented in Table 1.  
The training epoch is a factor that affects the performance of deep networks. A sufficient training epoch ensures convergence of the entire training process and leads to the best network performance under specific parameter configurations. Figure 12 illustrates the training set loss and precision during the 50-epoch training employed in this work. As can be seen from Figure 13a, YOLO-V3 was accompanied by drastic changes throughout the training process and does not converge at the end of the training. In contrast, SSD, Faster R-CNN, RetinaNet and our method all achieved convergence. Among them, SSD is relatively oscillatory, requiring about 26 epochs to reach convergence; the loss curves of Faster R-CNN, RetinaNet and our method are more stable, all converging after the 12 th epoch. Combining Figure 13a,b, it can be seen that among all the compared \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Backbone** & **Loss Function** \\ \hline Faster R-CNN & ResNet-50 & MSE \\ SSD & VGG-16 & MSE \\ YOLO-V3 & Darknet-53 & MSE \\ RetinaNet & ResNet-50 & Focal loss \\ Ours & ResNet-50 & MSE \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Competitor methods. methods, YOLO-V3 achieves the lowest accuracy of about 0.8 and the largest loss value of about 3.2; the detection performance of SSD is only better than YOLO-V3 with a precision of about 0.86 and a loss value of about 1.1. The detection performance of RetinaNet is slightly better than that of Faster R-CNN, where the precision of Faster R-CNN is about 0.93 and the loss value is about 0.5. and The precision of RetinaNet is about 0.96 and the loss value is about 0.08. Our method achieves the highest detection precision (about 0.98) and the lowest loss value (about 0.04). Figure 14 depicts the detection results of all competitor methods on some example images of the parcel test set. It is worth mentioning that we set the threshold value of IoU to 0.5 to ensure credible parcel detection. For images involving a single-layered parcel stack, when the number of parcels is less than five, e.g., sample 5, all methods detect all parcels. However, when the parcels exceed five, e.g., sample 2, Faster R-CNN, RetinaNet, and SSD miss the parcels at the upper corner, while YOLO-V3 misses all five parcels. In the presence of shape-similar target interference, e.g., sample 4, Faster R-CNN identifies the keyboard box as a parcel, RetinaNet identifies the blank space in the upper right corner as a parcel, while SSD and YOLO-V3 have more missed detections. Considering images with two parcel layers, e.g., sample 1, Faster R-CNN, RetinaNet, SSD, and YOLO-V3, all have multiple false and missed detections, while our method only misidentifies the position of the parcel in the upper left corner. In summary, compared with Faster R-CNN, RetinaNet, SSD, and YOLO-V3, our method is more accurate, robust, and adaptable to the case of different stacks of parcels. To validate the effectiveness of our proposed method on the entire parcel dataset, we quantitatively evaluated the experimental results on the test set. As listed in Table 2, our method achieves an AP (0.5) of 0.9742, which is higher than the other methods. This indicates that our method detects parcels in images more accurately. Moreover, although our FPS is 22.83, which is lower than SSD and YOLO-V3, it is similar to Faster R-CNN and RetinaNet and, most importantly, meets the practical application requirements. In addition, we conducted experiments to evaluate the performance of the proposed method under different backbones, including VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-101 and MobileNet-V2.