Progressive Dynamic Queries Reformation-Based DETR for Remote Sensing Object Detection

Abstract

Object queries-based detection transformer (DETR) makes remarkable achievements in object detection. However, most object queries design approaches are initialized with only one input and shared among all samples, which may result in the propagation of probing errors and lacking understanding of remote sensing objects with diversified structures and complex backgrounds. To address these issues, this letter proposes a progressive dynamic queries reformation (PDQR) for DETR-based remote sensing object detection, which consists of multihierarchical dynamic object queries and progressive reformation. A group of unique object queries are dynamically weighted, which are then fed into the current stage of decoder to reform the updated object queries of previous stage. This progressive reformation can suppress error propagation from earlier stages and reduce the influences of backgrounds. Moreover, the dynamic object queries can enhance the awareness ability of fine-grained features. PDQR can be flexibly plugged into various DETRs. The experimental results on different benchmark datasets demonstrate the superiority of PDQR over several state-of-the-art DETRs. Specifically, the PDQR-based DINO achieves 95.9%, 80.2%, and 97.3% mAPs on NWPU VHR-10, DIOR, and RSOD datasets, respectively.