文献类型：期刊文献

英文题名：Dynamic Background Reconstruction via MAE 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] Automation Department, School of Information Science and Engineering, East China University of Science and Technology, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230018388)

语种：英文

外文关键词：Infrared imaging

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 Vision Transformers 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 F1-score on the two ISTD datasets, MFIRST (64.10%) and SIRST (75.01%). Copyright ? 2023, The Authors. All rights reserved.