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Trigonometric-Coded Refined Detector for High Precision Oriented Object Detection
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AI
DOI：10.1109/LGRS.2023.3313884.png)
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Abstract
En 中文
Oriented object detection in aerial images is a crucial link in earth observation. As a special parameter in oriented object representation, the angle is the key to achieving high-precision detection. However, the widely used regression-based methods suffer from boundary discontinuity problems due to the periodicity of the angle. To address this issue, we proposed a novel angle prediction method called fixed step trigonometric coder (FSTC). Exploiting the innate periodicity of trigonometric functions, FSTC can encode angles cyclically in a succinct, continuous, and uniform manner. Based on FSTC, we designed a single-shot oriented object detector, namely, a trigonometric-coded refined detector (TRDet), for high-precision object detection in real-time. TRDet consists of two modules: the anchor optimization module (AOM) and the object detection module (ODM). AOM employs FSTC to generate high-quality rotated anchors. In ODM, a dynamically weighted loss (DWL) was proposed to make the model focus on hard samples with higher angle deviation. Extensive experiments on DOTA and HRSC2016 show that both FSTC and TRDet can achieve competitive performance compared with peer works.
Keywords:
Aerial images
deep learning
oriented object detection
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