# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 1 Introduction
### Observer Camouflage Metrics  
Considering that camouflage aims to reduce detection likelihood, evaluating human visual search effectiveness in field experiments under strictly managed setups is typically seen as the benchmark for assessing camouflage effectiveness. Nonetheless, conducting  
Figure 1: Flowchart of the present study. For each experiment, we either generated (Experiments 1 and 2) or recorded stimuli (Experiment 3)—each consisting of a single person (at multiple locations and sometimes poses) in a camouflage suit against a specific background. Human participants completed two tasks: a visual search task, yielding search times (i.e., time required to locate the person), and a visual conspicuity task, measuring identification conspicuity (i.e., the maximum angle at which the person remains distinguishable from the background). Both metrics are established indicators of camouflage effectiveness. In parallel, we used YOLOv4-tiny to detect the person in each scene, extracting confidence levels for classification as a ’person.’ The study examines whether YOLO’s confidence scores predict camouflage efficiency as determined by human performance. field experiments is expensive, time-intensive, demands significant manpower, and poses logistical challenges. Additionally, the range of conditions under which camouflage's effectiveness can be evaluated is restricted. Therefore, field trials are frequently substituted with lab-based studies that employ photos and videos from field experiments. These lab tests allow for complete control over influencing factors. However, there can be significant discrepancies between human performance in these artificial lab settings and real-world environments. Such differences might stem from the simulated conditions not accurately reflecting real-world factors like resolution [6], brightness, color fidelity [7], and dynamic range, among others, the impact of which remains largely unexplored. An effective and practical alternative to extensive search and detection studies is the concept of visual object conspicuity. This approach measures how much an object's visual characteristics stand out against its immediate environment, based on differences in aspects such as size, shape, brightness, color, texture, depth perception, movement, and the amount of surrounding clutter. An object is more readily spotted if it contrasts sharply with its background, with detection speed increasing as this contrast becomes more pronounced [8; 9; 10]. Consequently, visual conspicuity is a key factor in determining the outcome of human visual search tasks. This concept can be defined by the maximum distance in the visual field at which an object in peripheral vision remains distinguishable from its surroundings. In other words, if an object is well camouflaged, then one needs to fixate close to the object to identify it, while an easy-to-distinguish object can be viewed far in the periphery. We have previously established and tested an efficient psychophysical method to measure this principle, requiring only a minimal number of participants to achieve statistical relevance [11]. This approach is quick, works on-site, assumes complete knowledge of the object and its location, and has proven to effectively predict human visual search behavior in both simple and complex scenarios [11; 12], including for both stationary [12] and moving objects [13], in laboratory settings [12] and real-world environments [12; 14]. Moreover, visual conspicuity can assess the detectability of camouflaged objects across standard visual, near infrared, and thermal spectra [14; 15; 16]. Depending on the specific criteria applied, it is possible to evaluate the conspicuity either for detection, which relates to an observer's subjective judgment of the object's visual distinction from its background, or for identification, which involves recognizing specific details at the object's location.