## 4 Results and Discussion
### Comparison with the State of Art Network  
Beyond the individual or end-to-end performance metrics of the proposed database and method, the comparison with the well-known networks is crucial. At this point, the uniqueness of both VHRShips and HieD doesn't easily enable the adaptation of previous  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Detection** & **Localization** & **Recognition** & **Identification** \\ \hline \multirow{4}{*}{falseNegative} & 2 & \multirow{4}{*}{(No. of images labeled as without ship(s) even with ship(s))} & 226 & \multirow{4}{*}{(No. of ships which are missed)} & 29 \\  & (No. of ships which are missed) & & couldn’t be detected \& & (No. of ships couldn’t be recognized as Navy) \\  & & & & be recognized) \\ \cline{3-4}  & \multirow{4}{*}{(invalid metric for this stage)} & 2 & \multirow{4}{*}{(No. of ships couldn’t be located, so couldn’t be localized)} & 7 \\  & & & & \\ \cline{3-4}  & \multirow{4}{*}{(No. of images labeled as with ship(s) even without ship (s))} & 2 & \multirow{4}{*}{(No. of ships wrongly recognized)} & 7 \\  & & & & \\ \cline{3-4}  & \multirow{4}{*}{(invalid metric for this stage)} & 2 & \multirow{4}{*}{(No. of ships which be located, so localized)} & 7 \\  & & & & \\ \cline{3-4}  & \multirow{4}{*}{(No. of ships which are considered as ship(s) even not)} & 2 & \multirow{4}{*}{(No. of ships wrongly recognized)} & 7 \\  & & & & \\ \cline{3-4}  & \multirow{4}{*}{(No. of images labeled as with ship(s) but stage)} & 2 & \multirow{4}{*}{(No. of ships being even not, so incorrectly recognized)} & 50 \\  & & & & \\ \cline{3-4}  & \multirow{4}{*}{(No. of images with ship(s)s)} & 2 & \multirow{4}{*}{(No. of ships truly recognized)} & 50 \\  & & & & \\ \cline{3-4}  & \multirow{4}{*}{(No. of images without ship(s))} & 2 & \multirow{4}{*}{(No. of images labeled as with ship(s) but none of ship localized)} & 2 \\ \cline{3-4}  & & & & \\ \cline{3-4}  & \multirow{4}{*}{(No. of ships which are not ship(s))} & 2 & \multirow{4}{*}{(No. of shipsdatabases to HieD or methods to VHRShips. The main bottlenecks are the end-to-end evaluation process, four levels for databases, and four stages for methods. On the other hand, to show the position of HieD among the well-known networks, YOLOv4 is adapted to the DLRI process and end-to-end evaluation process. The results of the comparison between HieD and YOLOv4 are given here. YOLOv4 is a one-stage detector network. It processes the image and provides objectiveness with bBox and the class of the target(s). Unlike YOLOv4, HieD presents two levels of classification. For this reason, YOLOv4 is trained with twenty-three classes (fifteen parent classes plus eight child navy classes) to make a complete comparison. The classification results are divided according to the groups used in the recognition and identification stages of HieD. While the objectiveness output of YOLOv4 corresponds to the detection stage, the localization stage is a common part of the two approaches. Fifteen civilian ship classes plus eight navy ship classes on behalf of the general navy class form the recognition stage. The results of eight navy ship classifications are accepted for the identification stage. Considering these pre-assumptions, YOLOv4 was trained with the same parameters, and the results are presented in Table 12.  
A visual representation of the comparison between the proposed HieD and YOLO v4 for inshorse regions is provided in Figure 4, and provided in Figure 5 for offshore regions. The detection performances of HieD and YOLOv4 in VHRShips are almost the same. For localization, HieD performs better than YOLOv4, even though it also uses the bBox localization feature of YOLOv4. The main reason behind this phenomenon is the number of classes used in the YOLOv4, which is twenty-three, while the YOLOv4 used in HieD deals only with two classes. In addition, YOLOv4 tries to tune classification for twenty-three classes while trying to reduce the bBox loss at the same time. This outcome is one of the reasons that HieD is proposed.