An internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images. Issue 4 (2nd October 2021)

• Record Type: Journal Article
• Authors: Zhang, Sihang; Shao, Zhenfeng; Huang, Xiao; Bai, Linze; Wang, Jiaming
• Is Part Of: Geo-spatial information science. Volume 24:Issue 4(2021)
• Page Start: 654
• Page End: 665
• Publication Date: 2021-10-02
• Subjects: Arbitrary orientated object detection -- optical remote sensing image -- single-shot -- deep learning; Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2021.1972772
• Languages: English
• ISSNs: 1009-5020

Abstract

ABSTRACT: 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 optimization, 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 optimization, we propose an IOU balanced loss that addresses the regression challenges related to arbitrary orientated bounding boxes. Experimental results on the DOTA and HRSC2016 benchmark datasets show that our proposed method outperforms selected methods.