# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 5 Discussion  
In general, YOLO was able to detect the persons both in photosimulations (Experiments 1 and 2) and in naturalistic stimuli (Experiment 3). The present study is not the first study illustrating that convolutional neural networks (YOLO) are capable of detecting camouflaged target objects in different environments [49; 50; 51]. Whereas the vast majority focused primarily on the detection algorithm performance, in the present study, we investigated whether YOLO is capable of assessing camouflage efficiency by comparing its performance with human observer data. Here, we found evidence that camouflage efficiency (as determined by YOLO's confidence rating: detection probability) correlated with human performance. More specifically, camouflage efficiency correlated positively with the object identification conspicuity and negatively with mean search time. However, the latter correlation was only significant for the photosimulation experiments (Experiment 2b) but failed to reach significance for the naturalistic setting (Experiment 3b). The absence of a significant correlation in the naturalistic setting may be attributed to the combination of a low number of trials (i.e., four) per image and, probably more important, the person being always highly salient. Indeed, the person was always easy to find, with mean search times between 500 and 750 ms, indicating that the person did pop out from their environment on every trial (regardless of their location) [8; 9; 52; 53]. In contrast, in the photosimulation experiment, the mean search time across the different images varied between 500 and 2000 ms, indicating that the search was more difficult for at least some of the images shown. As a result, fewer trials are required to find significant differences across the different images used for the photosimulation than in the naturalistic setting. However, in the visual search literature, a frequently used rule of thumb is that at least 20 trials are required to estimate search performance for a particular condition in a lab setting [54; 55] and at least 60 trials in a more applied setting [48]. Nevertheless, the strong \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Search \(\times\)_p(Detection)_**} & \multicolumn{2}{c}{**Conspicuity \(\times\)_p(Detection)_**} & \multicolumn{2}{c}{**Conspicuity \(\times\) Search**} \\ \hline
**Subject** & **Spearman r** & \(p\) & **Spearman r** & \(p\) & **Spearman r** & \(p\) \\ \hline
1 & \(-\)0.11 & 0.70 & 0.71 & \(<\)0.01 & \(-\)0.20 & 0.49 \\
2 & \(-\)0.10 & 0.73 & 0.48 & 0.08 & \(-\)0.42 & 0.13 \\
3 & \(-\)0.42 & 0.13 & 0.55 & \(<\)0.05 & 0.09 & 0.75 \\
4 & \(-\)0.24 & 0.42 & 0.66 & \(<\)0.01 & \(-\)0.02 & 0.95 \\
5 & \(-\)0.24 & 0.42 & 0.10 & 0.74 & 0.16 & 0.59 \\
6 & \(-\)0.17 & 0.56 & 0.55 & \(<\)0.05 & \(-\)0.20 & 0.50 \\ \hline mean & \(-\)0.30 & 0.30 & 0.58 & \(<\)0.05 & \(-\)0.05 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spearman correlations between the detection probability (i.e., YOLO performance), search performance, and the identification conspicuity performance for each participant separately. _p_-values were not Bonferroni-corrected. positive correlation between the object conspicuity measure and YOLO's performance is a good indicator that YOLO is suitable for estimating camouflage efficiency. It is interesting that the detection of persons by YOLO was influenced by whether we superimposed shading on the person or not. It was easier to detect a person with shading than without. This suggests that not only is the outline or silhouette of the person an important factor, but also the articulation of other body parts, such as arms, which became visible when adding shading (see Figure 3). From the perception literature, it is well known that humans are particularly sensitive to the typical configuration of body parts.