Open Access

ARTICLE

A Novel Filtering-Based Detection Method for Small Targets in Infrared Images

by Sanxia Shi, Yinglei Song^*

School of Automation, Jiangsu University of Science and Technology, Zhenjiang, 212001, China

* Corresponding Author: Yinglei Song. Email:

Computers, Materials & Continua 2024, 81(2), 2911-2934. https://doi.org/10.32604/cmc.2024.055363

Received 25 June 2024; Accepted 30 September 2024; Issue published 18 November 2024

Abstract

Infrared small target detection technology plays a pivotal role in critical military applications, including early warning systems and precision guidance for missiles and other defense mechanisms. Nevertheless, existing traditional methods face several significant challenges, including low background suppression ability, low detection rates, and high false alarm rates when identifying infrared small targets in complex environments. This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach. The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs. In the first step, the infrared image is filtered by a new filter kernel and the results of filtering are normalized. In the second step, an adaptive thresholding method is utilized to determine the pixels in small targets. In the final step, a fuzzy C-mean clustering algorithm is employed to group pixels in the same target, thus yielding the detection results. The results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional approaches. Compared with the traditional method of state of the arts detection method, the detection accuracy of the four sequences is increased by 2.06%, 0.95%, 1.03%, and 1.01%, respectively, and the false alarm rate is reduced, thus providing a more effective and robust solution.

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Keywords

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