MGNet: A Remote Sensing Oriented Object Detector Based on Multicascaded Feature Selection and Geometric Constraints

High interclass similarity and diverse geometric configurations in high spatial resolution remote sensing images pose significant challenges for remote sensing object detection. To address these challenges, this study proposed a novel remote sensing-oriented object detector MGNet based on multicascaded feature selection and geometric constraints, designed to enhance discriminative feature representation and optimize oriented bounding box regression. First, we designed a novel backbone multicascaded feature selection network (MCFSNet) to improve interclass separability by jointly leveraging and fusing contextual information and multiscale features. Second, we designed a geometric-constrained probabilistic loss, which introduced an angular offset factor and a geometric perception factor to refine angular prediction accuracy of square targets and shape sensitivity. To systematically evaluate angular localization performance, we constructed a multiscale plane remote sensing object detection dataset (MPRSOD) with arbitrary-orientated annotations and proposed the angle error rate (AER), the first dedicated evaluation metric for quantifying angular prediction errors. Extensive experiments on DIOR-R, DOTA V1.5, MAR20, and MPRSOD datasets demonstrate that MGNet obtains competitive mean average precision (mAP) results while maintaining computational efficiency.