Detection of Camouflaged Objects Using Convolutional Neural Network
Keywords:
Camouflage detection, decamouflagingAbstract
The goal of camouflage is to blend a foreground object's texture into the background image frame’s texture. Camouflage detection methods, also known as decamouflaging methods, are typically used to locate foreground objects that are concealed in the background image. One of the key uses of machine vision in this field is the identification of camouflaged objects in photographs for both military and non-military applications. The camouflage detection technique put forth in this study can be utilized to find one or more target objects in camouflaged pictures. In this study, a neural network is employed to recognize the item in the image of camouflage. Images with naturally occurring and man-made camouflaged objects are used in experiments. Naturally camouflaged objects are equivalent to animals and artificially, that is, man-made camouflaged objects are equivalent to people in the actual world. The performance of the proposed technique is validated using precision and recall.
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