Object-specific edge detection (OSED) aims to detect object edges in an image along with classify the edge into object or non-object. It prunes edges which are not belonging to the object class for following processing, such as, feature matching for object detection, localization and three-dimensional reconstruction. In this paper, an OSED method that combines region proposal detectors with deep supervision nets to identify object-specific edges is proposed. It minimizes errors of object proposal by learning from hidden layers. Additionally, it combines features from different scales to detect object edges. In order to evaluate the performance of the OSED, we present two datasets which are captured in real scenes. The OSED method demonstrates a high accuracy of 90% and a high speed of 0.5 s for an image whose size is 512 × 448 pixels on the proposed datasets.

特定对象边缘检测（OSED）旨在检测图像中的对象边缘，并将边缘分类为对象或非对象。它修剪不属于对象类别的边缘以进行后续处理，例如用于对象检测，定位和三维重建的特征匹配。本文提出了一种OSED方法，该方法将区域提议检测器与深度监督网络相结合，以识别特定于对象的边缘。通过从隐藏层学习，可以最大程度地减少对象建议的错误。此外，它结合了不同比例尺的特征以检测物体边缘。为了评估OSED的性能，我们提出了两个在真实场景中捕获的数据集。OSED方法显示出90％的高精度和0的高速。