# Embedded Deep Learning for Ship Detection and Recognition
## 6 Conclusions  
Considering the challenges of ship detection and recognition, this paper proposes an embedded deep learning system for ship detection and recognition named ESDR-DL. It first locates the bow, cabin and stern of the ship using DNet, and then recognizes them by a classification network named CNet. Finally, voting is used to recognize the ship identification. We implement the ESDR-DL with an embedded architecture which supports real-time video processing. We have deployed ESDR-DL at  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Camera** & **Rain-S** & **Rain-P** & **Ran-R** & **Smog-S** & **Smog-P** & **Smog-R** & **Dusk-S** & **Dusk-P** & **Dusk-R** \\ \hline entrance camera-1 & 46 & 0.53 & 0.45 & 114 & 0.36 & 0.20 & 203 & 0.79 & 0.72 \\ entrance camera-2 & 46 & 0.45 & 0.38 & 114 & 0.31 & 0.18 & 203 & 0.81 & 0.75 \\ entrance camera-3 & 39 & 0.46 & 0.36 & 99 & 0.29 & 0.21 & 211 & 0.82 & 0.74 \\ entrance camera-4 & 40 & 0.45 & 0.32 & 103 & 0.26 & 0.15 & 225 & 0.83 & 0.73 \\ inside port camera-1 & 70 & 0.55 & 0.41 & 91 & 0.34 & 0.21 & 293 & 0.80 & 0.72 \\ inside port camera-2 & 58 & 0.52 & 0.45 & 69 & 0.29 & 0.17 & 233 & 0.75 & 0.70 \\ inside port camera-3 & 132 & 0.49 & 0.39 & 155 & 0.39 & 0.23 & 254 & 0.80 & 0.74 \\ inside port camera-4 & 140 & 0.54 & 0.45 & 169 & 0.35 & 0.19 & 254 & 0.82 & 0.76 \\ inside port camera-5 & 129 & 0.48 & 0.43 & 143 & 0.32 & 0.24 & 223 & 0.80 & 0.75 \\ inside port camera-6 & 129 & 0.51 & 0.41 & 143 & 0.25 & 0.12 & 223 & 0.84 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance of ship recognition in bad weather.  
Figure 7: Ships in bad weather: The top is the ships in bad weather, and the bottom is the process results.  
Dongying port, China. It has been running stably in the past year, which shows the effectiveness of our solution. In the future, we will adopt a multi-model data fusion approach [23; 24] to improve the recognition accuracy.  
Conceptualization, H.Z. and W.Z.; methodology, H.Z.; validation, H.Z., H.S. and B.X.; formal analysis, W.S.; investigation, B.X.; resources, W.Z.; writing-original draft preparation, H.Z.; writing-review and editing, H.Z. and H.S.; visualization, W.Z. and B.X.; project administration, W.Z.  
This research was funded by the Key Research Program of Shandong Province under Grant No. 2017 GGX10140 and the National Natural Science Foundation of China under Grant No. 61309024.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ESDR-DL & Embedded Ship Detection and Recognition using Deep Learning \\ DCNet & Detection and Classification Network \\ DNet & Detection Network \\ CNet & Classification Network \\ DCNN & Deep Convolutional Neural Network \\ RPN & Region Proposal Network \\ RCNN & Region-based Convolutional Neural Network \\ ROI & Region Of Interest \\ DSP & Digital Signal Processing \\ PDA & Personal Digital Assistant \\ FPGA & Field-Programmable Gate Array \\ TDA3x SoC & Threat Discovery Appliance \\ SSD & Single Shot MultiBox Detector \\ YOLO & You Only Look Once \\ \end{tabular}