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.

中文翻译：

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用于定向物体检测的短边偏移


定向目标检测在图像处理方面取得了重大进展。与水平检测方法相比，定向检测器在回归中添加方向参数来定位目标。然而，现有的旋转和四边形表示并不适合定向两阶段方法来生成有效的定向提案。在这封信中，我们提出了一种新的框架来检测定向对象，称为短边偏移检测（SSEDet）。受圆定理的启发，我们提出了一种从水平矩形到有向矩形的变换方法，以准确描述有向对象。具体来说，我们利用短边相对于右上角顶点的偏移来表示矩形的方向。与水平矩形相比，定向矩形的表示参数仅多了一个方向参数。在定向参数的作用下，可以实现表示参数与定向矩形的一一对应。在常用数据集上的实验结果验证了 SSEDet 可以生成高质量的定向提案。