基于特征融合与边界修正显著性目标检测

Feature Fusion and Boundary Correction Network for Salient Object Detection

显著性目标检测旨在寻找图像中的视觉显著区域。现有的显著性目标检测方法已经展现出强大的优势,但依然在尺度感知和边界预测方面具有局限性。首先,各类场景中的显著目标存在诸多尺度,使算法难以适应不同尺度变化。其次,显著目标往往具有复杂的轮廓,这使边界像素点的检测变得更为困难。针对以上问题,文中提出了基于特征融合与边界修正的显著性目标检测网络,该网络基于特征金字塔,提取了不同层次显著特征。首先针对目标的尺度多样性设计了由多尺度特征解码模块组成的特征融合解码器,通过逐层融合相邻层特征,提高了网络对目标尺度的感知能力。同时设计了边界修正模块学习显著目标的轮廓特征,以生成边界清晰的高质量显著图。在5个常用显著性目标检测数据集上进行实验,结果表明所提算法在平均绝对误差、F指标和S指标3项定量指标上均能取得较优的结果。

Saliency object detection aims to find visually significant areas in an image.Existing salient object detection methods have shown strong advantages,but they are still limited by scale perception and boundary prediction.First of all,there are many scales of salient objects in various scenes,which makes it difficult for the algorithm adapt to different scale changes.Secondly,salient objects often have complex contours,which makes detection of boundary pixels more difficult.To solve these problems,this paper proposes a feature fusion and boundary correction network for salient object detection.This network extracts salient features at different levels on the feature pyramid.Firstly,a feature fusion decoder composed of multi-scale feature decoding modules is designed for the scale diversity of the object.By fusing the features of adjacent layer by layer,the network's ability to perceive the scale is improved.At the same time,a boundary correction module is designed to learn the contour features of salient objects to generate high quality salient images with clear boundaries.Experimental results on five commonly used salient object detection datasets show that the proposed algorithm can achieve better results on the average absolute error,F index and S index.