# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 5 Discussion
For instance, studies using point-light displays have shown that observers can reliably detect human figures based solely on the motion of a few key points (most of the time joints), even in cluttered scenes (see, e.g., [56]). This implies that the brain uses information about body part location and movement to detect people. Even without motion cues, the human visual system can detect objects (including people) based on small, diagnostic image fragments--even without seeing the whole object [57]. As expected, detection by YOLO (and humans) decreases with decreasing target size. This can be viewed as a decrease in the signal-to-noise in the system [58], in which the evidence (features) for the presence of the target diminishes with size, with the evidence especially presented by the combination of features and less by the low-level features themselves (that may occasionally resemble elements in the background). The effect of shading disappeared with distance as well. That the effect of shading disappeared with distance makes perfect sense, as for the human eye, details such as the trunk of a tree, the arms of a person, or the letters on billboards typically can no longer be resolved at larger distances. Although the distance effect was rather consistent and present for the vast majority of conditions, it was interesting to observe that YOLO had more difficulties in finding a person wearing a 1/f fractal camouflage suit derived from the bush ROI when the person was nearby than when he was further away. A feasible explanation for this counterintuitive finding is that the textual structures (pattern elements) of the camouflage suit gave the percept that some diagnostic body parts, such as an arm, were missing when the person was nearby but not when he was far away. This occurred even in the condition where we superimposed a shading on the person. A feasible explanation is that the shading had little effect when using a 1/f fractal pattern with colors derived from the bush/desert ROI and not from the urban ROI. In the former condition, the shading effect was minimized due to the dark textures in the camouflage suit (see the shading for both 1/f fractal patterns in Figure 3c). That YOLO has difficulties with detecting persons nearby is intriguing, as this suggests that the whole or at least a large part of the body needs to be clearly visible to detect the person. Simply camouflaging a part of the body (by using a high-contrast pattern like in the present study) seems to be enough to trick YOLO but not the human observer (who is capable of detecting persons based on smaller parts of a body). To enable the evaluation of camouflage in a realistic setting, it would be good to realistically model shading and the different parts of the camouflage suit such that the arms (and other body parts) become more articulated (and thus easier to detect for YOLO). The present study also replicates earlier findings. For instance, the camouflage efficiency significantly improved when the colors of the camouflage suit (e.g., bush/desert) consisted of colors taken from its immediate background (bush/desert environment) than when it consisted of colors taken from another background urban environment. This color effect was observed for both human behavior and for YOLO's behavior. Furthermore, again, human performance and YOLO's performance both indicated that the 1/f fractal pattern is a more efficient camouflage pattern compared to a uniform average color. Interestingly, as is clear from the detection probability heatmaps, even though the camouflage suit and the person's pose were kept constant for each condition, the camouflage efficiency not only depended on the distance but also on where the person was presented on the horizontal plane. This variance not only illustrates that the camouflage efficiency for a particular camouflage suit depends on the local contrast with its immediate environment, but it also illustrates the value of using YOLO as a tool to measure camouflage efficiency.