Oriented Object Detection via Contextual Dependence Mining and Penalty-Incentive Allocation

Oriented object detection in aerial images has made significant advancements propelled by well-developed detection frameworks and diverse representation approaches to oriented bounding boxes. However, within modern oriented object detectors, the insufficient consideration given to certain factors, like contextual priors in aerial images and the sensitivity of the angle regression, hinder further improvement of detection performance. In this article, we propose a dual-focused detector (DFDet), which simultaneously focuses on the exploration of contextual knowledge and the mitigation of angle sensitivity. Specifically, DFDet contains two novel designs: a contextual dependence mining network (CDMN) and a penalty-incentive allocation strategy (PIAS). CDMN constructs multiple features containing contexts across various ranges with low computational burden and aggregates them into a compact yet informative representation that empowers the model for robust inference. PIAS dynamically calibrates the angle regression loss with a scalable penalty term determined by the angle regression sensitivity, incentivizing model to boost regression capacity for large aspect ratio objects challenging to be localized accurately. Extensive experiments on four widely-used benchmarks demonstrate the effectiveness of our approach, and new state-of-the-arts for one-stage object detection in aerial images are established. Without bells and whistles, DFDet with ResNet50 achieves 74.71% mean of average precision (mAP) running at 23.4 frames per second (FPS) on the most widely-used DOTA-v1.0 dataset. The source code is available at
https://github.com/DDGRCF/DFDet
.