Open Access

ARTICLE

Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

Ning Li^*, Shucai Huang, Daozhi Wei

Air and Missile Defense College, Air Force Engineering University, Xi’an, 710051, China

* Corresponding Author: Ning Li. Email:

(This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)

Computers, Materials & Continua 2023, 77(3), 3511-3531. https://doi.org/10.32604/cmc.2023.045987

Received 14 September 2023; Accepted 08 November 2023; Issue published 26 December 2023

Abstract

This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet (ISTD-CenterNet) network for detecting small infrared targets in complex environments. The method eliminates the need for an anchor frame, addressing the issues of low accuracy and slow speed. HRNet is used as the framework for feature extraction, and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects. A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared image. Besides, an improved sensory field enhancement module is designed to leverage semantic information in low-resolution feature maps, and a convolutional attention mechanism module is used to increase network stability and convergence speed. Comparison experiments conducted on the infrared small target data set ESIRST. The experiments show that compared to the benchmark network CenterNet-HRNet, the proposed ISTD-CenterNet improves the recall by 22.85% and the detection accuracy by 13.36%. Compared to the state-of-the-art YOLOv5small, the ISTD-CenterNet recall is improved by 5.88%, the detection precision is improved by 2.33%, and the detection frame rate is 48.94 frames/sec, which realizes the accurate real-time detection of small infrared targets.

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Keywords

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