EAR-Net: Edge-Guided Adapter-Enhanced Recursive Fusion Network for Small-Object Detection in Remote Sensing Imagery

Abstract

Small-object detection in remote sensing imagery is challenging because tiny targets often suffer from large-scale variation, weak boundaries, and heavy background interference. Although modern one-stage detectors are efficient, repeated downsampling often removes fine boundary details. As a result, dense tiny objects may become difficult to separate in deep feature maps. Meanwhile, background textures such as roads, roofs, and shadows may look similar to small targets, leading to false detections. To address these problems, we propose EAR-Net, an edge-guided adapter-enhanced recursive fusion network built on YOLOv11n. EAR-Net improves small-object representation from three aspects. First, the multiscale edge-guided (MSEG) feature injection module uses Sobel-initialized learnable edge operators to provide explicit boundary guidance. Second, the adapter-enhanced C2PSA module introduces lightweight bottleneck adapters into the attention branch to suppress high-frequency background noise while preserving global context. Third, the recursive feature fusion mechanism introduces a recursive “focus–distribute–refocus–fuse” process to reduce cross-scale spatial misalignment. Experiments on NWPU VHR-10, DIOR, RSOD, and HRRSD show that EAR-Net improves detection accuracy and localization precision while maintaining a practical balance between accuracy and efficiency.