SRDet: self-regularized object detection framework for UAV perspective

Abstract

Object detection in unmanned aerial vehicle (UAV) imagery presents unique challenges, including extreme scale variations, highly imbalanced spatial distribution of objects, and computational constraints inherent to drone platforms. To address these issues, this paper introduces a novel self-regularized object detection framework specifically designed for UAV perspectives. The proposed method adopts LSNet as the backbone network, guided by the "See Large, Focus Small" principle, to enhance feature extraction capability for objects of varying scales under wide-angle UAV views. The framework incorporates two key components: a Self-Regularized Temporal Ratio Encoder (SRTR Encoder) that stabilizes training and improves bounding box quality by leveraging temporal aspect ratio constraints, and an Entropy Constrained Sampler (EC Sampler) that selects informative hard negative samples based on information entropy, thereby suppressing less relevant background regions and increasing training efficiency. Extensive experiments conducted on the VisDrone benchmark demonstrate that the proposed approach significantly outperforms existing methods, achieving a notable improvement in mean Average Precision (mAP) while simultaneously reducing computational costs. For instance, when integrated with the CEASC detector, the framework increases mAP from 25.4% to 28.0% while reducing GFLOPs from 105.90 to 92.61. The results validate the effectiveness of our method in addressing the specific challenges of UAV-based object detection, offering a balanced solution for accuracy and efficiency in resource-constrained scenarios.