基于密集连接的层次化显著性物体检测网络

Hierarchical Salient Object Detection Network with Dense Connections

全卷积神经网络(FCN)在许多密集标记任务中表现出色。最近,基于FCN的显著性物体检测模型得到了快速发展。在本文中,提出了一种基于FCN的像素级显著物体检测网络。该模型首先通过自动学习多层次多尺度的显著性特征进行显著性粗略预测,包括颜色、纹理、形状和物体性等特征。然后采用密集连接的特征提取模块来进一步提取更丰富的特征信息。此外,本文引入跳层结构以提供更好的特征表示,利用深层产生的物体性语义特征引导浅层输出的显著性图更好定位显著性对象,最后,网络使用加权融合模块以组合各种特征。为了进一步提高显著图的空间连贯性并生成清晰轮廓,本文采用条件随机场(CRF)模型作为后处理步骤以优化网络预测得到的加权显著性图。整个网络以粗糙到精细的方式进行显著性检测,在5个公开的常用基准数据集上进行性能评估,并与10个具有代表性的算法进行比较,证明了本文所提模型的稳健性和有效性。

Fully convolutional neural networks(FCNs)have shown outstanding performance in many dense labeling tasks.FCN-like salient object detection models haven mostly developed lately.In the work,we propose a novel pixel-wise salient object detection network based on FCN by aggregating multi-level feature maps.Our model rst makes a coarse prediction by automatically learning various saliency cues,including color and texture contrast,shapes and objectness.Then a densely connected feature extraction block is adopted to further extract rich features at each resolution.Moreover,skip-layer structure is introduced for providing a better feature representation and helping shallow side outputs locate salient objects.In addition,a weighted-fusion module is utilized to combine multi-level features.Finally,a fully connected CRF model can be optimally incorporated to improve spatial coherence and contour localization in the fused saliency map.The whole architecture works in a coarse to ne manner.Evaluations on ve benchmark datasets and comparisons with 10 state-of-the-art algorithms demonstrate the robustness and e ciency of our proposed model.