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arXiv Computer Vision By Wei Lu, Junjie Li, Feifei Sang, Si-Bao Chen

SPEANet: Structural Prior Enhanced Attention Network for Parameter-Efficient Remote Sensing Object Detection

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SPEANet is a parameter‑efficient backbone for remote sensing object detection that incorporates fixed structural operators to enhance contour and frequency responses. It uses stage‑specific prior extraction and context‑conditioned response modulation, assigning smoothed contour and directional modeling to shallow features and a compact approximation‑detail interaction to deeper stages. Experiments on five benchmarks, including DOTA‑v1.0, DOTA‑v1.5, and DIOR‑R, show that SPEANet achieves high mAP scores (up to 78.55%) with only 23.0 M total parameters, of which the backbone accounts for 5.97 M.

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