# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 4 Experiment 3: YOLO vs. Human Camouflaged Person Detection on Naturalistic Images
### Results  
The results of Experiment 3a are illustrated in Figure 9. Here, five random samples from the movie are shown (the person is always present). If YOLO detected the person, the green square signifies its location with a given detection probability as shown in the graph. The graph illustrates the detection probability for each movie frame. The movie can be downloaded online from [[https://osf.io/vpcer/files/osfstorage/62](https://osf.io/vpcer/files/osfstorage/62) fd376b7b16410c0c12f3b1]([https://osf.io/vpcer/files/osfstorage/62](https://osf.io/vpcer/files/osfstorage/62) fd376b7b16410c0c12f3b1) (accessed on 29 April 2025).  
Figure 9: Five random samples from the movie. The yellow line identifies the sample in the movie (with corresponding detection probability). If YOLO detected the person, the green square signifies its location with a given probability as shown in the graph. The graph illustrates the detection probability for each movie frame. Note that the pose of the person varied during the movie and that sometimes a part of the body was not visible.  
Figure 9 illustrates that YOLO did a good job of finding the person. Even though motion detection is not incorporated (i.e., our version of YOLO considers each image as a static individual image), the person was detected in the vast majority of samples. Interestingly, poses such as kneeling down and (partly) hiding behind a tree affected performance significantly.  
Figure 10 illustrates the results from Experiment 3b. Figure 10a illustrates the number of responses as a function of the localization error. Trials were excluded from further analyses if the participants were not able to localize the person accurately (i.e., the localization error was >150 pixels) and responded too slowly (RTs > 10 s). As a result, we excluded 0.6% of the trials (only localization errors).  
Figure 10b illustrates the search performance as a function of detection probability for each frame used in Experiment 3b. The negative correlation between the search time (human performance) and detection probability (YOLO performance) was not significant: Spearman r = \(-\)0.30, \(p\) = 0.296. Figure 10c illustrates the identification conspicuity as a function of the detection probability for each frame tested. As is clear from this figure, like in Experiment 2b, the identification conspicuity performance correlated positively with the detection probability, Spearman r = 0.58, \(p\) < 0.05, indicating that YOLO predicted the  
Figure 10: Results of Experiment 3b. (**a**) Number of responses as a function of the localization error. Trials were excluded from further analyses if the participants were not able to localize the person accurately (i.e., localization error > 150 pixels; see the red dotted line). (**b**) Mean search time as a function of the detection probability for each stimulus. (**c**) Mean identification conspicuity (in degrees visual angle: dva) as a function of the detection probability for each stimulus. (**d**) Mean identification conspicuity as a function of the mean search time for each stimulus. Note that each datapoint reflects an image used in the experiment.  
conspicuity performance quite well. Table 2 illustrates the correlations for each participant separately. Interestingly, even though we had only a few trials for each participant, the identification conspicuity correlated positively with the detection probability for four out of six participants. The search performance did not correlate with the identification conspicuity performance: Spearman r = \(-\)0.05, \(p\) = 0.880 (see Figure 10d). This is most likely due to the low number of search trials in this study [48].  
To summarize, the results of Experiment 3 are in line with those from Experiment 2, indicating that the detection probability correlated positively with mean identification conspicuity.