## 5 Conclusions  
Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of the salient object in underwater images, we proposed a dual-fusion active contour model with semantic information. The proposed method extracted the saliency target contour by fusing the local and global intensity and extracted the original image contour information by the local image fitting model to correct the saliency information deviation. We verified the superiority of the dual-fusion active contour model with semantic information by qualitative comparison and quantitative comparison. The qualitative comparison results show that the proposed model can effectively suppress the interference of noise and can more accurately extract the contour of the intensity inhomogeneity region. Moreover, we also verified that the missing saliency target contour can be effectively corrected by the contour information of the original image. The quantitative comparison results show that the salient object contour extraction results of the proposed model achieve the highest accuracy and lowest error. Therefore, the proposed model can effectively solve the problem that conventional active contour models cannot extract the salient object contour due to the lack of semantic information and provides support for underwater target tracking, underwater image restoration, and other technologies.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **(i)** & **(ii)** & **(iii)** & **(iv)** & **(v)** & **(vi)** & **(vii)** & **(viii)** \\ \hline LBF & 0.7434 & 0.5195 & 0.0510 & 0.7863 & 0.2585 & 0.2769 & 0.3403 & 0.2386 \\ LGIF & 1.0012 & 0.5978 & 0.1414 & 0.7304 & 0.1237 & 0.2283 & 0.2092 & 0.3109 \\ LIF & 0.8355 & 0.4713 & 0.0761 & 0.3578 & 0.1209 & 0.2938 & 0.5013 & 0.1483 \\ RMPCM & 1.1478 & 0.3300 & 0.0962 & 1.0167 & 0.1167 & 0.2137 & 0.3200 & 0.9286 \\ Our & 0.2709 & 0.2649 & 0.0452 & 0.3411 & 0.0776 & 0.2007 & 0.2060 & 0.0571 \\ \hline \end{tabular}
\end{table}
Table 4: The ER results of LBF, LGIF, LIF, RMPCM, and our method in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **(i)** & **(ii)** & **(iii)** & **(iv)** & **(v)** & **(vi)** & **(vii)** & **(viii)** \\ \hline LBF & 9.5723 & 3.6588 & 3.0737 & 12.8131 & 2.1789 & 4.4882 & 6.9886 & 5.3132 \\ LGIF & 7.9119 & 3.7481 & 3.4620 & 9.6148 & 2.2546 & 4.1299 & 7.4210 & 6.3514 \\ LIF & 6.0811 & 4.0945 & 2.5343 & 7.4206 & 2.9782 & 4.2036 & 10.2057 & 4.9874 \\ RMPCM & 10.5081 & 4.3048 & 5.1594 & 12.9181 & 2.3604 & 6.0406 & 7.9683 & 3.9875 \\ Our & 2.3695 & 3.2604 & 1.9161 & 4.5302 & 1.2455 & 2.7715 & 5.9417 & 2.9702 \\ \hline \end{tabular}
\end{table}
Table 3: The MAE results of LBF, LGIF, LIF, RMPCM, and our method in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **(i)** & **(ii)** & **(iii)** & **(iv)** & **(v)** & **(vi)** & **(vii)** & **(viii)** \\ \hline LBF & 1.3148 & 1.7739 & 13.9989 & 1.2499 & 3.5065 & 3.4029 & 2.7005 & 3.9709 \\ LGIF & 0.9778 & 1.5494 & 6.0806 & 1.3423 & 6.7686 & 4.0834 & 4.1945 & 3.0709 \\ LIF & 1.1707 & 1.9462 & 10.1875 & 2.6965 & 6.9244 & 3.2240 & 1.8872 & 6.1722 \\ RMPCM & 0.8569 & 2.7046 & 8.6031 & 0.9690 & 7.0274 & 4.2727 & 2.8823 & 1.0500 \\ Our & 3.4725 & 3.3199 & 15.3967 & 2.7586 & 10.3096 & 4.6516 & 4.3053 & 14.0682 \\ \hline \end{tabular}
\end{table}
Table 5: The DR results of LBF, LGIF, LIF, RMPCM, and our method in Figure 5.  
**Author Contributions:** Conceptualization, methodology and software, S.Y.; writing--review and editing, J.W.; supervision, funding acquisition, Z.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (2018 YFC0810500).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.