Military camouflage classification with mask R-CNN algorithm

Camouflage, which is used as an art of hiding by living things in nature, started to be used in the military field in the 19th century. When factors such as different nations, environment and climate are considered, we come across camouflages in various colors and patterns. While the similarity of the camouflaged area with the background makes segmentation difficult, it becomes difficult to classify each camouflage pattern due to the cut of the fabric and the different locations of the pattern pieces on each soldier. There are different studies in the literature that are referred to as camouflage or pattern classification. The mentioned studies are in the form of segmentation of camouflaged object or classification of camouflaged objects of different types. Since the segmented and classified objects in this study are camouflaged soldiers, what is expected from the deep learning algorithm is to classify the objects mainly according to the camouflage pattern, not their outlines. In this study, 1233 images of soldiers in camouflage were collected for 5 countries and the military camouflage classification problem was solved with the Mask R-CNN algorithm, which is widely used today for object detection, segmentation and classification, and the importance of CNN algorithms was proved with such a difficult problem.