Instance Segmentation and Classification of Armoured Fighting Vehicles

Abstract

A two-phase deep learning prototype for detection and classification of armoured fighting vehicles was developed. The two phases consist of an instance segmentation model and an image classification model. The instance segmentation model detects and outlines armoured fighting vehicles. These images are then piped into an image classification model which classifies the vehicles by model. The instance segmentation model reached a mean average precision of 0.399 at intersection over union of range 0.5 to 0.95. The image classification model reached 84.5% top 1 accuracy. Overall, adding synthetic images to the training data for instance segmentation yields a model with about 10% higher overall average precision.