# A Fast Maritime Target Identification Algorithm for Offshore Ship Detection
## 4 Experiments
### Results Analysis  
The results of target recognition by the model are shown in Figure 4. A target detector on a ship with good performance can provide maritime authorities with an objective reference for data visualization and reduce the ship's collision risk due to human negligence.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Version** \\ \hline CPU & Intel(R) Xeon(R) Gold 6130 CPU @ 2.10 GHz \\ GPU & NVIDIA TAITAN RTX 24 G \\ OS & Ubuntu 20.04 \\ python & 3.7.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Platform configuration information.  
For the first image of Figure 4A, the far ship targets were not labeled in detail at the beginning of the experiment, which was subject to an 'accident' of erroneous recognition. The recognition effect by the FMTI algorithm was so accurate that it surpassed the labeling, i.e., the number of targets identified successfully was more than the number of the ones labeled manually. Similarly, the hull pieces in the second image are partially overlapping but can still be distinguished. In the third photo, the ships are separated, and this allowed for the best recognition.  
The FMTC algorithm has a good performance of recognition for not only the multitarget tasks but also the simple or single target(s) tasks, like in Figure 4B.  
In particular, ResNet-101 [47] was introduced as a backbone network for cross-sectional comparison of models, denoted by Res101. Table 4\({}^{\prime}\)s data is rounded; however, this does not affect the overall assessment. Unavailable or useless data is indicated by /.  
Table 4 was obtained in the 2017 COCO validation set, and Table 5 was acquired from self-built datasets. The data is generated on an identically equipped device. Following a comprehensive analysis of Table 4, the FMTI and YOLOF models were chosen to be applied to the self-built dataset. The results are given in Table 5, \(Score_{confidence}=0.5\).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Name** & **FMTI** & **YOLOF** & **YOLOF + SFMF** & **YOLOF (Res101)** & **YOLOF (Res101) + SFMF** \\ \hline
**FPS** & 39 & 40 & 38 & 25 & 29 \\
**FLOPs** & 91 G & 86 G & 91 G & / & / \\
**Model\_parameter** & 48 M & 44 M & 49 M & 64 M & 68 M \\
**mAP** & \textgreater{}37.7\% & 37.7\% & 37\% & 36\% & 37\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Preliminary comparison results of different combinations.  
Figure 4: Recognition results of FMTI: (**A**) multi-objective, (**B**) simple or single objective.  
As shown in Table 4, we acquired 37 percent mAP (YOLOF + SFMF) and 36 percent mAP (YOLOF (Res101)), respectively. FMTI achieves more than 0.7 percent mAP improvement (Baseline: YOLOF + SFMF or YOLOF (Res101) + SFMF) and better than YOLOF (Res101) over 1.7 percent mAP, respectively. Furthermore, YOLOF received 37 percent mAP, an increase of one percent above the YOLOF (Res101) mAP. In terms of mAP, FMTI exceeds YOLOF and the other models, although it has a slightly lower FPS than YOLOF, which does not affect the processing performance of the FMTI model.  
The results are clearly shown in Table 5. Additionally, it is worth highlighting that the improvement of mAP is over 7%, which is significant. The computational power has been advanced in parallel with mAP. The model is frequently improved along with memory changes. The accompanying increase in model parameters is so normal that it is within acceptable ranges.  
More particularly, when the FMTI algorithm is applied for maritime monitoring to provide early warning for potential danger signals in offshore areas proactively, the occurrence probability of maritime accidents must be reduced. The FMTI model proposed in this paper applies to maritime target detection, meanwhile, it also can broad application prospects in the fields of maritime rescue, maritime traffic monitoring, and maritime battlefield situational awareness and assessment.