RSDM: Research of Rotation and Small Object Detection Models for Remote Sensing Images

Abstract Instance objects captured in aerial imagery display a range of orientations and exhibit significant scaling differences in their dimensions. Recent researches mainly address the above problems in terms of label assignment strategies and extended backbone spatial receptive fields, however, the former may still introduce lower quality samples, while the latter usually introduces considerable background noise. In this paper, the proposed Multiscale Convolutional Kernels Combined with Bi-Level Routing Attention for backbone Feature Extraction Network (MKBANet) employs multi-scale convolutional kernels to extract feature information at different scales, capture local context information, and combine with a Bi-Level Routing Attention (BRA) to capture remote context information and reduce computational complexity. In addition, an adaptive constrained oriented point set strategy (ACOPSS) is proposed. The adaptive point set constraint strategy is introduced in training, and then the quality metric function is utilized to select the top k high-quality directed point set samples in training, and finally the outliers are penalized. 81.23% mAP on DOTA dataset, the efficacy of the introduced approach is demonstrated.