RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors focus on detecting tiny targets in infrared images, which is important for areas like security and rescue. They point out that current methods struggle to clearly identify the edges of these small targets because of their low visibility and typical modeling approaches. To improve this, the authors designed a new network called RPCASSM that treats the background and target separately using two specialized modules. Their method uses spatial scanning techniques to better capture background patterns and target details, leading to more accurate detection of small infrared targets. Tests on standard datasets show that their approach works well.
infrared small target detectionstate space modelrobust principal component analysis (RPCA)background state space module (BSSM)target state space module (TSSM)spatial probe scanning mechanism (SPCM)deformable prompt scanning mechanism (DPCM)sparsityspatial domainedge modeling
Authors
Pingping Liu, Aohua Li, Yubing Lu, Jin Kuang, Tongshun Zhang, Qiuzhan Zhou
Abstract
The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime rescue and so on. Due to the low occupancy of these targets in long-distance imaging, the mainstream visual state space model is inefficient and difficult to accurately model the target edge. The existing infrared state space models do not deviate from the mainstream visual state space structure framework from the structural properties of infrared small targets. In order to solve this problem, this paper proposes the RPCASSM network based on the model paradigm of robust principal component analysis(RPCA), which aims to design the background state space module(BSSM) and the target state space module(TSSM) by the nature of the infrared small target in the spatial domain. The BSSM aims to use the saliency of spatial heterogeneous signals to design a spatial probe scanning mechanism(SPCM) to model background information. The TSSM designs a deformable prompt scanning mechanism(DPCM) by using the sparsity and local highlight of the target to focus on the deformable space of the target for state space modeling. According to the above design, we effectively solve the problem that the existing mainstream vision state space model is difficult to accurately model the edge structure of infrared small target. Experimental results on the existing benchmark data sets prove the effectiveness of the RPCASSM design. Our code will be made public at \href{https://github.com/PepperCS/RPCASSM}{RPCASSM}.