Deep multilayer network for automatic targeting system of gun turret

Abstract

Military vehicles are important part to maintain territories of a country. Military vehicle is often equipped with a gun turret mounted on top of the vehicle. Traditionally, gun turret is operated manually by an operator sitting on the vehicle. With the advance of current robotic technology an automatic operation of gun turret is highly possible. Notable works on automatic gun turret tend to use features that are manually designed as an input to a classifier for target tracking. These features can cause less optimal parameters and require highly complex kinematic and dynamic analysis specific to a particular turret. In this paper, toward the goal of realizing an automatic targeting system of gun turret, a gun turret simulation system is developed by leveraging fully connected network of deep learning using only visual information from a camera. It includes designing convolutional layers to accurately detect and tracking a target with input from a camera. All network parameters are automatically and jointly learned without any human intervention, all network parameters are driven purely by data. This method also requires less kinematic and dynamic model. Experiments show encouraging results that the automatic targeting system of gun turret using only a camera can benefit research in the related fields.