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.