A Frequency-Aware and Multiscale Aligned Framework for Fine-Grained Object Detection in Remote Sensing Imagery

Abstract

Fine-grained object detection in remote sensing imagery is challenged by small targets, subtle interclass differences, and cross-scale feature mismatch. This article proposes a frequency-aware and multiscale aligned framework for YOLO-based detectors. FAENet separates and enhances low-frequency structural information and high-frequency details before backbone feature extraction. DSAF aligns adjacent pyramid features through joint pooling and interpolation to reduce scale mismatch in the neck. ScalSeq with ASF Attention aggregates multiscale features and refines the $P_{3}$ branch to strengthen small-target discrimination. Experiments on MAR20, HRSC2016, and ShipRSImageNet cover four YOLO families, three model scales, and three input resolutions. The full configuration improves detection accuracy in most settings and generalizes across aircraft and ship targets. Among the evaluated full configurations, YOLOv5-M achieves the best accuracy–cost balance: its average accuracy increases from 0.524 to 0.550, while the parameter count rises from 25.076 to 26.023 M and inference time from 1.4 to 2.2 ms, yielding the highest accuracy–cost gain ratio of 0.087. Comparisons with representative non-YOLO detectors further confirm its practical efficiency.