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Short-Side Excursion for Oriented Object Detection
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AI
DOI：10.1109/LGRS.2022.3200110.png)
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摘要
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Oriented object detection has achieved a significant progress in image processing. Compared with horizontal detection methods, oriented detectors add the orientation parameter in regression to locate objects. However, the existing rotation and quadrilateral representations are not appropriate for oriented two-stage methods to generate efficient oriented proposals. In this letter, we propose a novel framework to detect oriented objects, termed short-side excursion detection (SSEDet). Inspired by the circle theorem, we propose a transformation method from horizontal rectangles to oriented ones to accurately describe oriented objects. To be specific, we exploit the offset of short sides relative to the top-right vertex to represent the orientation of rectangle. Compared with the horizontal rectangle, the representation parameters of oriented rectangle have only one more orientation parameter. Under the action of the orientation parameter, the one-to-one correspondence between representation parameters and oriented rectangle can be realized. Experimental results on commonly used datasets verify that the SSEDet can generate high-quality oriented proposals.
Keyword:
Proposals
Feature extraction
Object detection
Detectors
Head
Rotation measurement
Remote sensing
Circle theorem
oriented object detection
oriented rectangle
short-side excursion (SSE)
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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