DSSNet: An Anchor-Free Rotated Object Detection Network With Dynamic Sample Selection for Remote Sensing Images

Object detection in remote sensing imagery requires precise localisation and identification of targets under challenging conditions. Facing the challenges of arbitrary target orientations, wide-scale variations, dense distributions, and small objects in remote sensing object detection, anchor-based methods suffer from inadequate rotated target representation using rectangular boxes. This necessitates excessive angle-specific anchors, leading to heavy computational overhead, severe sample imbalance, and slow speeds unsuitable for mobile deployment. To address these accuracy-efficiency trade-offs, we propose DSSNet: an anchor-free rotated object detection network with dynamic sample selection for remote sensing images. DSSNet replaces traditional backbones with the parameter-efficient ConvNeXt-T and utilises an FPN for accelerated multi-scale feature extraction. During prediction, it employs a shape-adaptive selection strategy combined with a contour point quality assessment strategy to dynamically refine target contour points, enabling real-time rotated object detection. The efficacy of DSSNet has been thoroughly validated through benchmark comparisons on diverse datasets. On the DOTA dataset, DSSNet clearly outperforms baseline methods in detection performance, achieving a mean Average Precision (mAP) of 76.97% and the fastest detection speed of 26.2 frames per second (FPS).