# Camouflaged Object Detection That Does Not Require Additional Priors
## 4 Experiments and Analysis
### Ablation Study  
To validate the effectiveness of each component and hyperparameter (\(\lambda\)) in our design, we conducted ablation experiments on three publicly available datasets. The results of these experiments are summarized in Tables 2-4. **Effectiveness of AFFM. ** From Table 2, we observe that Model **5** outperforms Model **4**. Specifically, utilizing camouflage cues generated by AFFM in Model **5** resulted in an average improvement of 0.5% in the \(S_{\alpha}\) metric, 1.33% in the \(E_{\phi}\) metric, and 1.23% in the \(F_{\phi}^{w}\) metric. Moreover, as depicted in Figure 6, the third column of results illustrates a comprehensive overview of the overall structure of the camouflaged object. This observation suggests that the camouflage cues provided by AFFM indeed contribute to a thorough understanding of the structure, aiding the model in distinguishing the camouflaged object from a similar background. Additionally, these cues furnish ample information for subsequent modules to filter and learn more precise details. **Effectiveness of FSM. ** As shown in Table 2, Model **4** demonstrates significant performance improvements over Model **2** on the three benchmark datasets. Specifically, the average increase in the \(S_{\alpha}\) indicator was 0.3%, the average increase in the \(E_{\phi}\) indicator was 0.26%, and the average increase in the \(F_{\beta}^{w}\) indicator was 0.8%. Furthermore, as illustrated in Figure 6, in comparison to the predictions in the third column, the predictions in the fourth column include more detailed features, such as the dog's legs in the first row. This observation suggests that the FSM contributes to enhancing the model's comprehension of the overall structure of camouflaged objects as well as mitigating uneven pixel distribution. **Effectiveness of ERM. ** In Table 2, the improved detection accuracy when comparing Model **3** with Model **5**, or when comparing Model **1** with Model **2**, clearly demonstrates that the inclusion of ERMs significantly enhances the model's ability to detect edge details. This is evident from the average increase of 0.56% in the \(S_{\alpha}\) metric, 0.3% in the \(E_{\phi}\) metric, and 0.36% in the \(F_{\beta}^{w}\) metric across the three benchmark datasets. In Figure 6, we observe that the predictions in the fifth column exhibit smoother edge features compared to those in the fourth column, as evident in the representation of the lizard abdomen in the third row of predictions. Additionally, the fifth column predictions present more comprehensive predictions for camouflaged objects, such as the legs of spiders in the second row. This  
Figure 6: Visual comparisons for showing the benefits of different modules. No.1, No.2, and No.3 depict the predictions generated by the model following AFFM, FSM, and ERM, respectively. (The images are from the CAMO [23] dataset. The effect images of the FSM and ERM modules are taken from the \(O_{1}\) branch in Figure 2). observation implies that the exploration of contextual information between the feature layers and the filtering of features based on the semantic relevance of different branches within the same feature to enhance fine-grained feature representation can effectively enhance the model's performance in detecting the edge information of camouflaged objects. **Sensitivity analysis on \(\lambda\). ** Analyzing the prediction results of the COD10K-test dataset in Table 3, we find that the model's prediction performance shows an increasing and then decreasing trend as the parameter \(\lambda\) increases. Specifically, when \(\lambda\) is less than or equal to 0.4, all the predictors show an increasing trend. When \(\lambda\) is greater than 0.5, all the predictors show a decreasing trend, and the predicted value reaches a peak when \(\lambda\) is at 0.5.