Search

Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object's orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD. [ABSTRACT FROM AUTHOR]