# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 6 Conclusions  
The application of camouflaged object detection (COD) techniques can improve the monitoring ability of water bodies and the environment, enhancing the protection and management of water resources and ecosystems. This contributes to the achievement of sustainable development. In this paper, we propose a cross-level iterative subtraction network (CISNet) for detecting camouflaged objects. Differently from most existing methods that integrate features from adjacent levels, the proposed cross-level iterative structure (CIS) iteratively integrates the highest-level features with their relevant lower-level features, making information complementation more efficient. Within CIS, we present a multi-scale strip convolution subtraction (MSCSub) module to extract difference information between cross-level features and fuse the cross-scale complementary features, which enhances the ability of a network to capture object details. Furthermore, we present an enhanced guidance attention (EGA) module to improve the segmentation accuracy by deeply mining local context information and capturing a broader range of relationships between different features. Through extensive experiments on benchmark datasets, our model demonstrates superior performance compared to other SOTA models in the COD task.  
Although our approach achieves good performance in certain scenarios, it may not perform optimally in highly complex backgrounds. Additionally, the current training dataset's lack of sufficient diversity may limit the model's adaptability to a wide range of camouflage strategies, potentially impacting its overall detection accuracy. Overcoming these limitations will be crucial for improving the robustness and applicability of the COD system in real-world situations. In future work, we intend to incorporate similarity-based  
Figure 9: Visualization results of downstream applications. From top to bottom: image (**first row**), ground truth (**second row**), and the results of CISNet (**third row**).  
learning techniques into COD to enhance the network's ability to refine its parameters by comparing the distinctive features of camouflaged objects with the standard visual attributes of generic objects.  
Conceptualization, T.H., X.L., S.C., T.Z. and J.C.; methodology, T.H., X.L., S.C., T.Z. and J.C.; software, T.H.; validation, T.H., S.C. and J.C.; formal analysis, T.H., C.Z., X.L., T.Z. and J.C.; investigation, C.Z., X.S., S.C. and T.Z.; resources, T.H. and X.L.; data curation, T.H., C.Z. and S.C.; writing--original draft preparation, T.H.; writing--review and editing, T.H., C.Z., X.L., X.S., S.C., T.Z. and J.C.; visualization, T.H.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (Grant Nos. 2023 YFC3209301, 2023 YFC3209201), the Excellent Post-doctoral Program of Jiangsu Province (Grant No. 2022 ZB166), and the Fundamental Research Funds for the Central Universities (Grant Nos. B230201007, B230204009, B220206006).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.