# Camouflaged Object Detection That Does Not Require Additional Priors
## 4 Experiments and Analysis
### Comparison with States of the Art  
We evaluated our method on three benchmark datasets: CAMO [23], COD10K [19], and NC4K [22]. To gauge the effectiveness of our approach, we employed four widely recognized and standardized metrics: MAE (\(\mathcal{M}\)) [31], weighted F-measure (\(F_{\beta}^{w}\)) [49], average E-measure (\(E_{\phi}\)) [50], and S-measure (\(S_{\alpha}\)) [51]. To demonstrate the effectiveness of our proposed CCGNet, we conducted a comparative analysis by comparing its prediction results with those of eleven state-of-the-art methods. The selected methods for comparison are EGNet [42], SCRN [52], F\({}^{3}\)Net [47], CSNet [53], BASNet [54], SINet [19], PFNet [36], S-MGL [21], BGNet [20], LSR+ [18] and C\({}^{2}\)FNet [37]. For a fair comparison, the prediction results of these methods were provided by the original authors or generated using models trained with open-source code. **Quantitative Evaluation. ** Table 1 summarizes the quantitative results of different COD methods on the three benchmark datasets. Our method outperforms previous methods in all four evaluation indicators on the three datasets. In particular, compared with the BGNet [20], our method shows an average increase of 1.13% in \(S_{\alpha}\), 0.16% in \(E_{\phi}\), and 0.1% in \(F_{\beta}^{w}\). Regarding the slightly lower training results observed in comparison to BGNet for certain datasets, we conducted a comprehensive analysis to identify the underlying reasons for these disparities. As illustrated in Figure 5, when we compare the prediction results of CCGNet with those of other models, we observe that CCGNet effectively mitigates the issue of uneven pixel distribution. However, this occasionally leads to a broader range of predicted objects, which may have a slight influence on specific evaluation metrics. Notable examples include the legs of insects in the first row of Figure 5 and the size of people in the fourth row. **Qualitative Evaluation. ** We qualitatively evaluated different COD algorithms on the CAMO dataset. As shown in Figure 5, our model provides more accurate details for the camouflaged object in the second row, which can be attributed to the precise localization and comprehensive appearance prediction. This result indicates that our FSM and ERM help the model separate the foreground background better and refine the edge prediction. For the fish in the third row, the current COD algorithm achieves accurate localization but predicts the overall structure poorly, resulting in uneven pixel distributions. Specifically, the phenomenon of missing object structures and labeling the foreground as the background occurs in the predictions. In contrast, our predictions show significantly less uneven pixel distribution, which suggests that our proposed AFFM is helpful for the model in understanding the overall structure of the object. For the fourth row of people, our model achieves more accurate localization and boundary prediction compared to other COD algorithms. These results show that our proposed CCGNet can understand the object's structure better and achieve finer edge prediction than COD algorithms using additional priors.