Infrared Small Target Detection Model with Multi-scale Fractal Attention

GU Yu; ZHANG Hongyu; SUN Shicheng

doi:10.11999/JEIT220919

Volume 45 Issue 8

Aug. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(8): 3002-3011

GU Yu, ZHANG Hongyu, SUN Shicheng. Infrared Small Target Detection Model with Multi-scale Fractal Attention[J]. Journal of Electronics & Information Technology, 2023, 45(8): 3002-3011. doi: 10.11999/JEIT220919

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GU Yu, ZHANG Hongyu, SUN Shicheng. Infrared Small Target Detection Model with Multi-scale Fractal Attention[J]. Journal of Electronics & Information Technology, 2023, 45(8): 3002-3011. doi: 10.11999/JEIT220919

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Infrared Small Target Detection Model with Multi-scale Fractal Attention

doi: 10.11999/JEIT220919

1.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2.
HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The Natural Science Foundation of Zhejiang Province (LY21F030010), The Science and Technology Program of Zhejiang Province (2019C05005)

Received Date: 2022-07-06
Rev Recd Date: 2022-10-28

Available Online: 2022-11-05

Publish Date: 2023-08-21

Abstract

Abstract

In order to improve the performance of infrared image small target detection, an end-to-end infrared small target detection model that integrates multi-scale fractal attention is designed by combining prior knowledge of traditional methods and feature learning ability of deep learning methods. Firstly, the procedure of accelerating the calculation of multi-scale fractal feature with deep learning operator is proposed based on analysis of this feature, which is suitable for detecting dim and small targets in infrared images. Secondly, the Convolutional Neural Network(CNN) is designed to obtain the target significance distribution map, and a multi-scale fractal feature attention module is proposed by combining the feature pyramid attention and pyramid pooling downsampling module. When embedding it into the infrared target semantic segmentation model, asymmetric context modulation is adopted to improve fusion performance of shallow features and deep features, and asymmetric pyramid non-local block is used to obtain global attention to improve infrared small target detection performance. Finally, the performance of the proposed algorithm is verified by experiments on the Single-frame InfRared Small Target(SIRST) dataset, where Intersection over Union (IoU) and normalized IOU(nIoU) reach 77.4% and 76.1%, respectively, which is better than the performance of the currently known methods. Meanwhile, the effectiveness of the proposed model is further verified by migration experiments. Due to the effective integration of the advantages of traditional methods and deep learning methods, the proposed model is suitable for infrared small target detection in complex environments.
- Infrared small target detection,
- Semantic segmentation,
- Multi-scale fractal feature,
- Attention mechanism,
- Pyramid pooling downsampling

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References(29)

References

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