AFEDet: A Symmetry-Aware Deep Learning Model for Multi-Scale Object Detection in Aerial Images

Abstract

Traditional convolutional neural networks face challenges in handling multi-scale targets in remote sensing object detection due to fixed receptive fields and simple feature fusion strategies, which affect detection accuracy. This study proposes an adaptive feature extraction object detection network (AFEDet). Compared with previous models, the design philosophy of this network demonstrates greater flexibility and complementarity. First, parallel dilated convolutions effectively expand the receptive field to capture multi-scale features. Subsequently, the channel attention gating mechanism further refines these features and assigns weights based on the importance of each channel, enhancing feature quality and representation ability. Second, the multi-scale enhanced feature pyramid network (MeFPN) constructs a structurally symmetrical bidirectional transmission path. It aligns multi-scale features in the same semantic space using linear transformation, reducing scale bias and improving representation consistency. Finally, the scale adaptive loss (SAL) function dynamically adjusts loss weights according to the scale of the target, guiding the network to learn features of different scale targets evenly during training and optimizing the model’s learning direction. The proposed architecture inherently integrates symmetry principles through its bidirectional feature fusion paradigm and equilibrium-seeking mechanism. Specifically, the symmetric structure of MeFPN balances information flow between shallow and deep features, while SAL applies a symmetry-inspired loss-weighting strategy to maintain optimization consistency across different scales. Experimental results show that, on the DOTA dataset, the proposed method improves the mAP by 7.12% compared to the baseline model.