ASIPNet: Orientation-Aware Learning Object Detection for Remote Sensing Images

Abstract

Remote sensing imagery poses significant challenges for object detection due to the presence of objects at multiple scales, dense target overlap, and the complexity of extracting features from small targets. This paper introduces an innovative Adaptive Spatial Information Perception Network (ASIPNet), designed to address the problem of detecting objects in complex remote sensing image scenes and significantly enhance detection accuracy. We first designed the core component of ASIPNet, an Adaptable Spatial Information Perception Module (ASIPM), which strengthens the feature extraction of multi-scale objects in remote sensing images by dynamically perceiving contextual background information. Secondly, To further refine the model’s accuracy in predicting oriented bounding boxes, we integrated the Skew Intersection over Union based on Kalman Filtering (KFIoU), which serves as an advanced loss function, surpassing the capabilities of the baseline model’s traditional loss function. Finally, we designed detailed experiments on the DOTAv1 and DIOR-R datasets, which are annotated with rotation, to comprehensively evaluate the performance of ASIPNet. The experimental results demonstrate that ASIPNet achieved mAP50 scores of 76.0% and 80.1%, respectively. These results not only validate the model’s effectiveness but also indicate that this method is significantly ahead of other most current state-of-the-art approaches.