An internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images

• 作者: Sihang Zhang Zhenfeng Shao Xiao Huang Linze Bai Jiaming Wang
• 来源期刊: 地球空间信息科学学报（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: Articles
• 页码范围: 654-665
• 页数: 12页
• 语种: 英文

摘要

Due to the bird's eye view of remote sensing sensors, the orientational information of an object is a key factor that has to be considered in object detection. To obtain rotating bounding boxes, existing studies either rely on rotated anchoring schemes or adding complex rotating ROI transfer layers, leading to increased computational demand and reduced detection speeds. In this study, we propose a novel internal-external optimized convolutional neural network for arbitrary orientated object detection in optical remote sensing images. For the internal opti-mization, we designed an anchor-based single-shot head detector that adopts the concept of coarse-to-fine detection for two-stage object detection networks. The refined rotating anchors are generated from the coarse detection head module and fed into the refining detection head module with a link of an embedded deformable convolutional layer. For the external optimiza-tion, we propose an IOU balanced loss that addresses the regression challenges related to arbitrary orientated bounding boxes. Experimental results on the DOTA and HRSC2016 bench-mark datasets show that our proposed method outperforms selected methods.