Infrared Small Target Detection Enhancement Using a Lightweight Convolutional Neural Network
Mridul Gupta, Jonathan Chan, Mitchell Krouss, G. Furlich, Paul Martens, Moses W. Chan, Mary L. Comer, Edward J. Delp
Abstract
Detection of small, point targets is fundamental in applications such as early warning systems, surveillance, astronomy, and microscopy. The presence of noise and clutter can make it challenging to detect small targets while minimizing false detections. This paper presents a method for infrared small target detection using convolutional neural networks. The proposed method augments a conventional space-based detection processing chain with a lightweight neural network to predict the probability that a detection is a target. The proposed network is trained on 7 × 7 pixel windows using both the image sequence and the respective background-subtracted images. Results show that our method improves probability of detection at low false detection rates.