FindNet: Can You Find Me? Boundary-and-Texture Enhancement Network for Camouflaged Object Detection

Camouflaged objects share very similar colors but have different semantics with the surroundings. Cognitive scientists observe that both the global contour (i.e., boundary) and the local pattern (i.e., texture) of camouflaged objects are key cues to help humans find them successfully. Inspired by the cognitive scientist’s observation, we propose a novel boundary-and-texture enhancement network (FindNet) for camouflaged object detection (COD) from single images. Different from most of existing COD methods, FindNet embeds both the boundary-and-texture information into the camouflaged object features. The boundary enhancement (BE) module is leveraged to focus on the global contour of the camouflaged object, and the texture enhancement (TE) module is utilized to focus on the local pattern. The enhanced features from BE and TE, which complement each other, are combined to obtain the final prediction. FindNet performs competently on various conditions of COD, including slightly clear boundaries but very similar textures, fuzzy boundaries but slightly differentiated textures, and simultaneous fuzzy boundaries and textures. Experimental results exhibit clear improvements of FindNet over fifteen state-of-the-art methods on four benchmark datasets, in terms of detection accuracy and boundary clearness. The code will be publicly released.

I. Introduction

Camouflage is a function that both predators and preys conceal themselves to deceive the visual perception systems of each other [1]. Although the concept of camouflage was first proposed by biologists in 1918 [1], more than 100 years later, sensory ecologists and cognitive scientists are still enthusiastic about this field, since camouflaged object detection (COD) has very practically meaningful applications including polyp segmentation [2], military anti-camouflage, search and rescue in extreme weather and discovery of endangered species, etc.