ONOA-YOLO: Orthogonal-Nesting Orientation-Aware Small Object Detection in Remote Sensing Imagery

Object detection in remote sensing imagery is a fundamental and crucial task within the remote sensing domain. However, small object detection in remote sensing images remains a challenge for SOTA methods. This is because small objects often have large aspect ratios, exhibit characteristic variations across different orientations, and are distributed within complex backgrounds. Current methods, which employ square convolutions, often retain excessive background information during feature extraction, leading to suboptimal detection performance. In this paper, an orthogonal-nesting orientation-aware small object detection network based on YOLOv10 is proposed to achieve small object detection in complex backgrounds. ONOA-YOLO employs an orthogonal-nesting module to enhance the features of small objects from multiple orientations, improving the sensitivity of the network to their intrinsic features. Additionally, it incorporates a background-suppression module based on large convolutional kernels. This module integrates the contextual information of small objects as guiding cues with other features, enabling the suppression of background interference while preserving the extracted small object features.