# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 3 Experiment 2: YOLO vs. Human Camouflaged Person Detection on Photosimulations  
It is known from the literature that a visual object captures attention when a specific feature, such as its color, shape, or orientation, deviates from its immediate background [33]. Similar results were reported in a military context, indicating that camouflage efficiency is best if a camouflage suit shares properties (such as colors and orientations) with its local background [29; 34]. In Experiment 2a, we examine whether YOLO is able to distinguish between good- and bad-performing camouflage patterns based on a comparison of their spatio-chromatic features with those of their local surroundings. Therefore, we manipulated both the pattern and the colors of the camouflage suit and presented the person in either an urban or bush/desert environment (like Experiment 1). The pattern was either an average color or consisted of a 1/f fractal pattern (see Figure 3). The colors used for the camouflage suits were either drawn from the bush/desert environment or from the urban environment. Therefore, we specified a region of interest (ROI) from which the colors were drawn for each environment separately (see Figure 3a). Throughout the experiment, we kept the person's pose fixed to prevent it from influencing [PERSON]'s performance. Moreover, we included another condition by adding shading to the person's camouflage suit since it is known that shading can affect [PERSON]'s performance (for example, for the detection of fruit; see [35; 36]). We manipulated the shade presence to examine whether shades influence [PERSON]'s performance, as they make it feasible to distinguish human body parts. Figure 3 illustrates the eight camouflage suits used in Experiment 2 (2 ROI's \(\times\) 2 camouflage patterns \(\times\) shade presence). We expect that [PERSON] has more difficulty in detecting the person when the camouflage suit consists of colors selected from the same background (e.g., a bush/desert camouflage suit in a bush/desert environment) than when it consists of colors selected from a different background (e.g., a bush/desert camouflage suit in an urban environment). Moreover, we expect that [PERSON] finds it more difficult to detect a person wearing a 1/f fractal pattern than one wearing an average-colored uniform, given that natural images also consist of similar 1/f structures [37; 38; 39; 40; 41; 42]. Furthermore, we expect that it is easier for [PERSON] to detect a person with shading than without shading, as the former is more realistic (and [PERSON] is trained to identify realistic persons). Finally, we expect the detection rate to decrease with distance and that the effect of shading ceases to exist with distance (as the articulation of details decreases with distance). Figure 3: (**a**) Region of interest (ROI) in the bush/desert (left panel) and the urban environment (right panel). (**b**) Camouflage patterns used in Experiment 2 without shading. (**c**) Camouflage patterns with shading. In Experiment 2b, we examine whether the detection probability provided by [PERSON] in Experiment 2a generalizes to human observers using a visual search and an object identification conspicuity paradigm. Thereto, we randomly selected a set of 64 images that were also used in Experiment 2a. Each image was shown four times (randomly mirrored) to obtain a better estimate of camouflage performance for each image, and participants performed two tasks. First, an image appeared on the screen, and participants were instructed to press the spacebar as soon as they found the person in the scene (i.e., search task). Subsequently, a mask replaced the image, and participants were asked to indicate the location of the person in the scene using their mouse to verify whether the participant correctly detected the person. Second, we measured the object identification conspicuity [11].