## 5 Conclusions  
In this work, our goal was to tackle the challenge of accurately segmenting camouflaged objects. We developed a new method for detecting camouflaged objects, in which we replaced the B6 and B7 layers of Effenet-B7 with the SWIN-T module and combined dense skip attention with object processing. In camouflaged object detection tasks, our proposed new model achieved good results on the benchmark datasets. In the camouflaged object task, our newly proposed model demonstrated good achievement on the benchmark datasets. In the next work, we plan to use this model in the field of polyp segmentation and continue to improve its performance to achieve better results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{4}{c}{**CHAMLEON (76 Images)**} & \multicolumn{4}{c}{**CAMO Test (250 Images)**} & \multicolumn{4}{c}{**COD10K-Test (2026 Images)**} \\ \cline{2-13}  & \(\mathbf{S_{\text{a}}\uparrow}\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{B}^{w}\uparrow\) & \(M\downarrow\) & \(\mathbf{S_{\text{a}}\uparrow}\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{B}^{w}\uparrow\) & \(M\downarrow\) & \(\mathbf{S_{\text{a}}\uparrow}\) & \(E_{\phi}^{ad}\uparrow\) & \(F_{B}^{w}\uparrow\) & \(M\downarrow\) \\ \hline (a) & B (our) & 0.694 & 0.833 & 0.526 & 0.076 & 0.634 & 0.745 & 0.457 & 0.127 & 0.659 & 0.775 & 0.420 & 0.066 \\ \hline (b) & Q ( B + T ) & 0.796 & 0.903 & 0.687 & 0.047 & 0.685 & 0.720 & 0.553 & 0.109 & 0.719 & 0.829 & 0.639 & 0.048 \\ \hline (h) & B + W (D + A) & 0.822 & 0.901 & 0.730 & 0.038 & 0.742 & 0.826 & 0.638 & 0.092 & 0.758 & 0.855 & 0.605 & 0.043 \\ \hline (l) & Ours & 0.853 & 0.914 & 0.776 & 0.028 & 0.802 & 0.865 & 0.727 & 0.069 & 0.802 & 0.890 & 0.676 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation analysis: “B” represents our baseline EfficientNet-B7, “Q” represents the model after replacing the Swin Transformer module in B6 and B7 of “B”, and “W” represents the addition of dense and attention mechanisms. The results demonstrate that each step yields effective outcomes, thereby substantiating the efficacy of our model.The \(\uparrow\) means that a larger value indicates better performance, the \(\downarrow\) means that a smaller value indicates better performance.  
Figure 2: The results of the ablation experiment.  
**Author Contributions:** Conceptualization, Y.W., W.K. and H.S.; methodology, Y.W., W.K. and H.S.; software, W.K. and H.S.; validation, Y.W., W.K. and H.S.; formal analysis, Y.W., W.K. and H.S.; investigation, Y.W.; resources, W.K. and H.S.; data curation, Y.W., W.K. and H.S.; writing--original draft preparation, Y.W., W.K. and H.S.; writing--review and editing, Y.W., W.K. and H.S.; visualization, Y.W., W.K. and H.S.; supervision, W.K. and H.S.; project administration, W.K. and H.S.; funding acquisition, W.K. and H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially supported by the National Key R & D Program of China (No. 2019 YFB21 01600), the National Natural Science Foundation of China (No. 61872025), the Macao Science and Technology Development Fund (File No. 0122/2023/AMJ), and the Open Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2021 ZX-03).  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.