Complicated Background Suppression of ViSAR Image For Moving Target Shadow Detection
원문 초록
The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR. The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
When SAM2 Meets Video Shadow and Mirror Detection
Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era
RenDetNet: Weakly-supervised Shadow Detection with Shadow Caster Verification
Timeline and Boundary Guided Diffusion Network for Video Shadow Detection
Language-Driven Interactive Shadow Detection
SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.