Decoding Camouflaged Object Detection: An Approach-Centric Review

Camouflaged Object Detection (COD) is a difficult task which involves detecting the objects that merge comfortably with the surrounding environment and at first glance no detection can be made of them by the human eye. This challenge is encountered because of contrast and intricate textures. This comparative analysis covers the different models and methods of doing the task of COD and compares the models in different measures including the Mean Absolute Error, [Formula: see text]-measure, structure-measure and enhanced-alignment measure. The method is very handy in the conservation of wildlife, surveillance and medical diagnosis with regards to security. It is a broad overview of the comparison of 35 different models founded on 10 different methodologies. It also opens up new possibilities to venture further in this area due to optimal better extraction and feature selection methods.