基于共享骨干的快速双分支拼接同步检测框架

doi:10.7527/S1000-6893.2025.32876

Abstract

Abstract:

In low-altitude remote sensing for real-time inspection， strong parallax， scale variations， and local distortions make the traditional “stitch-then-detect” serial paradigm prone to feature redundancy and error cascading， and make it difficult to unify global and local geometric transformations， which in turn limits speed and weakens robustness. To address this， we propose Fast Stitching and Detection Network （FSDNet）， a fast dual-branch stitching and synchronous detection framework built on a shared backbone. The framework adopts a pretrained detection backbone as a unified encoder and embeds an attention-guided local context correlation module in the stitching branch to explicitly regress fine-grained geometric flow fields from shared features. In addition， two collaborative branches are designed for estimating the transformation fields： a global homography branch for coarse alignment and a local thin-plate spline branch for fine alignment. These are combined with transformation-guided detection box rectification and intensity-adaptive fusion to enhance geometric-semantic consistency. Experiments on UDIS-D and Warped AU-AIR demonstrate that， while maintaining high-quality stitching， the proposed method improves Frames per Second （FPS） by about 66% compared with typical serial baselines and achieves superior object detection performance on Warped AU-AIR， validating the efficiency and practicality of the approach.

Key words: remote sensing, image stitching, object detection, feature sharing, thin-plate spline transformation

CLC Number:

V279

Zifeng YANG, Xia XU, Bin PAN. Fast dual-branch stitching and synchronous detection framework based on shared backbone[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(10): 532876.

Figures/Tables 8

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References 26

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方法	PSNR				SSIM				FPS	计算量/GFLOPs
方法	简单	中等	困难	平均值	简单	中等	困难	平均值	FPS	计算量/GFLOPs
I_3×3	15.87	12.76	10.68	12.86	0.530	0.286	0.146	0.303	<2.00
UDHN^［9］	18.86	16.03	13.21	15.75	0.554	0.322	0.197	0.331	10.32	40.3
CUDHN^［26］	16.37	13.86	11.30	13.59	0.394	0.152	0.056	0.183	10.54	18.8
UDIS^［10］	25.16	20.96	18.36	21.17	0.834	0.669	0.495	0.648	12.12	14.5
PDIS^［19］	30.19	25.84	21.57	25.43	0.933	0.875	0.739	0.838	11.43	244.5
FSDNet	28.66	24.34	20.29	24.01	0.912	0.833	0.669	0.791	17.83	13.6

方法	PSNR				SSIM				FPS	mAP@0.5/%
方法	简单	中等	困难	平均值	简单	中等	困难	平均值	FPS	mAP@0.5/%
UDHN+YOLOv11n	17.71	15.43	11.24	14.65	0.417	0.195	0.113	0.224	9.22	27.8
CUDHN+YOLOv11n	14.76	11.96	10.51	12.26	0.294	0.113	0.037	0.125	9.47	25.3
UDIS+YOLOv11n	24.44	20.74	18.25	21.01	0.793	0.702	0.610	0.695	11.52	31.7
PDIS+YOLOv11n	27.65	23.84	21.35	24.16	0.841	0.789	0.691	0.763	10.43	32.1
FSDNet	26.31	23.57	21.16	23.81	0.831	0.753	0.662	0.746	17.27	34.2

共享骨干	ACCL	双分支结构	FPS	PSNR	SSIM
×	×	×	16.27	21.96	0.671
√	×	×	17.41	22.05	0.683
√	×	√	17.08	23.17	0.761
√	√	×	18.11	22.47	0.703
√	√	√	17.83	24.01	0.791

Fast dual-branch stitching and synchronous detection framework based on shared backbone

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