Infrared Target Detection using Shallow CNNs

Convolutional Neural Networks can solve the target detection problem satisfactorily. However, the proposed solutions generally require deep networks and hence, are inefficient when it comes to utilising them on performance-limited systems. In this paper, we study the infrared target detection problem using a shallow network solution, accordingly its implementation on a performance limited system. Using a dataset comprising real and simulated infrared scenes; it is observed that, when trained with the correct training strategy, shallow networks can provide satisfactory performance, even with scale-invariance capability.