# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 3 Experiment 2: YOLO vs. Human Camouflaged Person Detection on Photosimulations
### Results  
Figures 4 and 5 illustrate the results of Experiment 2a for the bush/desert and urban background environment, respectively. Here, the detection probability heatmap is shown for each of the eight camouflage suits used. Figure 6a,b illustrate the detection probability as a function of object height (i.e., distance) for each camouflage suit used in the bush/desert and urban environment (collapsed over the horizontal plane), respectively. Even though for each panel the camouflage suit was always the same, the error bars represent a variance of detection probability over the horizontal plane. This variance is most likely a result of the heterogeneity of the environments. Indeed, the object-background similarity varied over the locations, making the same person highly visible at certain locations and invisible at other locations. This is consistent with the human visual search literature, showing that it is more difficult to find an object that is similar to its background than one that is dissimilar to its background. Furthermore, it is clear from these figures that, in general, the proportion of detected objects decreased with distance. This is in line with the results of our previous behavioral study using the same background images [29]. However, the 1/f camouflage suit with bush/desert colors revealed a somewhat different pattern of results for both backgrounds used. It is clear here that the detection probability did not decrease with distance but instead increased withdistance for the urban background and was rather low for the bush/desert environment in general. A feasible explanation for these deviating results is that the 1/f camouflage suit was generated as a whole, without taking important properties of a human being (like having arms, legs, etc.) into account. As a result, YOLO did not classify a large person as a 'person'. It is interesting to see that for more distant persons, the performance of the 1/f pattern was rather similar to that of the average uniform. Furthermore, it is also interesting that the detection probability increased when shading was added to the camouflage suits (compare the red lines with the black lines in each panel). This makes perfect sense, as adding shading results in more realistic 'persons' by emphasizing the shape characteristics of a human being. That the effect of shading disappeared over distance also makes perfect sense, as environmental details (such as a trunk of a tree or a person's hand) that are clearly visible when observed nearby can no longer be resolved at larger distances. Figure 4: Detection probability heatmaps in the bush/desert background. (**a**) Mean detection probability (i.e., the confidence score generated by YOLO) when the person was wearing a uniform in an average color derived from a bush/desert ROI. The panels on the left indicate the detection probability when no shading was added, whereas shading was added for the right panels. (**b**) Mean detection probability when the person was wearing a 1/f fractal camouflage pattern derived from a bush/desert ROI. (**c,d**) Same as panels (**a,b**) but for an urban ROI. The persons were located at naturalistic locations for both background environments (so that they were not presented in the air, tree, etc.). Due to this restriction, the persons were smaller for the bush/desert background (ranging between 14.82 and 201.28 pixels) than for the urban background (ranging between 24.37 and 214.20 pixels). Figure 6c,d illustrate the overall mean detection probability for each camouflage suit for both backgrounds. However, unlike the data presented in Figure 6a,b, we collapsed the results over all locations with the constraint that the height of the person was between 24.37 and 201.28 pixels for both background environments, thus enabling a comparison of the camouflage suits across the two different background conditions. Figure 5: Detection probability heatmaps in the urban background.