# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 5 Discussion
### Limitations  
In this study, we compared the performance of both humans and the pretrained YOLOv4-tiny model for the detection of camouflaged persons. Since YOLOv4 was introduced, several newer versions have appeared that either prioritize balancing the tradeoff between speed and accuracy rather than focusing on accuracy [59; 60], improve the accuracy of person detection in conditions with occlusion [49; 61; 62; 63; 64; 65; 66; 67; 68] or camouflage [51; 69], or enhance the detection of camouflaged objects in general [70]. In contrast, the study reported here was performed to investigate whether YOLO can predict human detection performance for camouflaged targets. If so, it can be used to evaluate the effectiveness of measures and materials designed to hinder the human visual detection of objects and persons. Given its relatively high accuracy, YOLOv4 still appears to be a valid tool for this purpose [64; 71]. Hence, it was not the aim of this study to compare the performance of YOLOv4 with other existing (camouflaged) person detection algorithms (that may well outperform human performance). Future research should investigate to what extent different types of convolutional neural networks can predict human detection performance. In its present form, YOLO's classification performance appeared especially sensitive to high-contrast, human shape-breaking camouflage (e.g., the 1/f fractal bush/desert camouflage pattern at short distances). The camouflage effectiveness of this pattern (when viewed close by) was overrated when compared to human camouflage measures (e.g., compared to that of the urban patterns in the urban environment). This shows that YOLO (in this form) deviates from human perception in some cases. Similarly, the squatting soldier in the movie was not detected by YOLO. These limitations of YOLOv4-tiny (e.g., sensitivity to body part articulation pose and high-contrast elements) suggest incorporating motion-based detection and custom training based on human data. Other cases have also been found in which CNNs are shown to deviate from human perception, e.g., CNNs can be fooled by adversarial patches to which humans are not susceptible [20; 72; 73]. This shows that it remains important to be aware of the differences between these algorithms and human perception, and these differences should be canceled as much as possible (e.g., by training a CNN not solely on detecting the whole body but also on parts of a human body) in order not to draw the wrong conclusions. An ablation study (in which components of the AI network are systematically removed [74]) may be performed to gain insight into the way the network processes the image and the elements it attends to. Another way to get insight into the attended elements is by using deconvolution [75; 76]. Such studies may reveal the ways in which the network algorithm deviates from human perception. A complication is that good camouflage should protect against different algorithms as well as humans [77], and what is optimally deceiving for one algorithm may not work for another algorithm [72]. However, more effort might be put towards deceiving those algorithms that may present an actual threat in military context, i.e., those that are more easily accessible and implementable (such as YOLOv4-tiny). In this study, we did not investigate the effects of environmental factors such as fog, rain, or low lighting on camouflage effectiveness. Each of these factors can significantly alter both the appearance of camouflage and the performance of object detection systems. Future work should, therefore, also study the impact of degraded image quality under inclement weather conditions. In some cases, camouflage that is otherwise ineffective--such as white clothing in a desert or urban setting--may become more effective under specific weather conditions (e.g., snowy or foggy environments). Promising solutions to address visibility issues in degraded imagery are, for instance, image dehazing methods.