计算机科学
人工智能
红外线的
计算机视觉
模式识别(心理学)
光学
物理
作者
Jingchao Peng,Haitao Zhao,Kaijie Zhao,Zhongze Wang,Lujian Yao
标识
DOI:10.1016/j.engappai.2024.108762
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%).
科研通智能强力驱动
Strongly Powered by AbleSci AI