Optimising colour for camouflage and visibility using deep learning: the effects of the environment and the observer’s visual system

Abstract

Avoiding detection can provide significant survival advantages for prey, predators, or the military; conversely, maximising visibility would be useful for signalling. One simple determinant of detectability is an animal’s colour relative to its environment. But identifying the optimal colour to minimise (or maximise) detectability in a given natural environment is complex, partly because of the nature of the perceptual space. Here for the first time, using image processing techniques to embed targets into realistic environments together with psychophysics to estimate detectability and deep neural networks to interpolate between sampled colours, we propose a method to identify the optimal colour that either minimises or maximises visibility. We apply our approach in two natural environments (temperate forest and semi-arid desert) and show how a comparatively small number of samples can be used to predict robustly the most and least effective colours for camouflage. To illustrate how our approach can be generalised to other non-human visual systems, we also identify the optimum colours for concealment and visibility when viewed by simulated red-green colour-blind dichromats, typical for non-human mammals. Contrasting the results from these visual systems sheds light on why some predators seem, at least to humans, to have colouring that would appear detrimental to ambush hunting. We found that for simulated dichromatic observers, colour strongly affected detection time for both environments. In contrast, trichromatic observers were more effective at breaking camouflage.

Author Summary

Being the right colour is important in a natural and built environment, both for hiding (and staying alive) or being seen (and keeping safe). However, empirically establishing what these colours might be for a given environment is non-trivial, depending on factors such as size, viewing distance, lighting and occlusion. Indeed, even with a small number of factors, such as colour and occlusion, this is impractical. Using artificial intelligence techniques, we propose a method that uses a modest number of samples to predict robustly the most and least effective colours for camouflage. Our method generalises for classes of observer other than humans with normal (trichromatic) vision, which we show by identifying the optimum colours for simulated red-green colour-blind observers, typical for non-human mammals, as well as for different environments, using temperate forest and semi-arid desert. Our results reveal that colour strongly affects detection time for simulated red-green colour-blind observers in both environments, but normal trichromatic observers were far more effective at breaking camouflage and detecting targets, with effects of colour being much smaller. Our method will be an invaluable tool, particularly for biologists, for rapidly developing and testing optimal colours for concealment or conspicuity, in multiple environments, for multiple classes of observer.