Filter design for small target detection on infrared imagery using normalized-cross-correlation layer

TL;DR

This paper presents a novel machine learning framework using a normalized-cross-correlation layer for designing filters tailored to detect small infrared targets, optimized for real-time FPGA implementation.

Contribution

It introduces a neural network-based approach with a new NCC layer and an FPGA-efficient MAD-NCC layer for infrared small target detection.

Findings

Filters effectively discriminate dim targets from clutter.

The MAD-NCC layer enables real-time FPGA deployment.

The method adapts filter design to specific infrared detection tasks.

Abstract

In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similarly to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on mid-wave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.