# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 1 Introduction
### YOLO Camouflage Metric  
State-of-the-art neural networks can detect and classify objects in digital imagery and provide a degree of certainty, or confidence, in their classification. Since this confidence level reflects an object's visual distinctness, we propose to adopt it as a measure of camouflage effectiveness. The benefit of this approach is that it requires no a priori knowledge of the nature, size, and position of the object of interest. Today, some of the best object detection results are achieved by models based on convolutional neural networks (CNNs). YOLO [24] is currently one of the fastest and most accurate object detection algorithms available. YOLO uses features learned by a deep convolutional neural network to detect specific objects in videos, live feeds, or images. It looks at the whole image at test time, so its predictions are informed by the global context in the image. The YOLO algorithm first divides the image into a grid. For each grid cell, it predicts a number of object bounding boxes (see the green squares in Figure 2a) with their associated class probabilities or confidence scores using a single feed-forward convolutional neural network. The confidence scores represent the degree of similarity of the detected object to each of the predefined object classes (such as person, train, cat, etc.) and reflect the accuracy of the model prediction. In this study, we validate the YOLO model for the assessment of the effectiveness of camouflaged persons by relating its output to human observer data (identification conspicuity and search performance) obtained in the lab. In the next sections, we first investigate whether the level of confidence predicted by YOLO can serve as a measure of camouflage effectiveness. To investigate whether the predicted certainty (confidence level) of an object's identity correlates with its degree of camouflage (i.e., whether it decreases with decreasing visual object distinctness), we apply YOLO to images of a single person situated in different environments and wearing clothing that provides different levels of camouflage. Then, we investigate whether the level of confidence predicted by YOLO also predicts human ratings of object identification conspicuity and human visual search performance. Since identification conspicuity and the level of confidence predicted by YOLO both increase with the visible amount of characteristic object details, we expect that both object distinctness measures will be correlated. A part of this work was previously published as a conference paper [25]. In this study, we focus primarily on static images to evaluate camouflage effectiveness. Previous research has shown that motion significantly undermines the benefits of camouflage--when an object begins to move, it becomes much more conspicuous, even if its appearance is otherwise perfectly matched to the background (as in the classic example of a well-camouflaged tiger revealing itself through movement; see [26; 27; 28]). That a moving object is easy to detect holds true both for human observers and for artificial vision systems. Given that motion inherently increases detectability (even under ideal camouflage circumstances), our current investigation is limited to static scenarios in which camouflage is most likely to succeed. By isolating this condition, we aim to assess the extent to which YOLO's  
Figure 2: Results of experiment 1. (**a**) Example of the bush/desert background (location: Soesterduinen) and urban background (location: Amsterdam) conditions. For illustrative purposes, multiple targets are shown. In the actual experiment, there was always one single target, and YOLO provided information about the detectability. In both panels, green squares signify that YOLO detected persons (with a given detection probability). Red arrows indicate the persons that YOLO was unable to detect. Note that the persons are scaled with distance.