文献类型：期刊文献

英文题名：Dynamic background reconstruction via masked autoencoders for infrared small target detection

作者：Peng, Jingchao[1];Zhao, Haitao[1];Zhao, Kaijie[1];Wang, Zhongze[1];Yao, Lujian[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Automat Dept, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2024

卷号：135

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20242416234659);WOS:【SCI-EXPANDED(收录号:WOS:001347754000001)】；

基金：This work was supported by the National Natural Science Founda-tion of China (NSFC) under Grant 62173143.

语种：英文

外文关键词：Infrared modality; Small target detection; Background reconstruction; Dynamic shift window; Masked autoencoder

摘要：Infrared small target detection (ISTD) under complex backgrounds is a difficult problem, for the differences between targets and backgrounds are not easy to distinguish. Background reconstruction is one of the methods to deal with this problem. This paper proposes an ISTD method based on background reconstruction called Dynamic Background Reconstruction (DBR). DBR consists of three modules: a dynamic shift window module (DSW), a background reconstruction module (BR), and a detection head (DH). BR takes advantage of masked autoencoders in reconstructing missing patches and adopts a grid masking strategy with a masking ratio of 50% to reconstruct clean backgrounds without targets. To avoid dividing one target into two neighboring patches, resulting in reconstructing failure, DSW is performed before input embedding. DSW calculates offsets, according to which infrared images dynamically shift. To reduce False Positive (FP) cases caused by regarding reconstruction errors as targets, DH utilizes a structure of densely connected Transformer to further improve the detection performance. Experimental results show that DBR achieves the best F 1-score on the two ISTD datasets, which can be widely used in anti-unmanned aerial vehicles, surveillance, and automatic driving systems. The model and dataset will be available at https://github.com/PengJingchao/DBR.