作者:王卫兵，张晓琢，邓强
Authors:WANG Wei-bing,ZHANG Xiao-zhuo,DENG Qiang摘要:摘要:针对现有的RGB-D图像显著性检测技术难以充分挖掘深度图像的有效信息，无法使RGB特征和深度特征有效融合的问题，提出了一种多分支主干监督网络下的RGB-D图像显著性检测方法。基于Resnet50网络获得两种图像的各层特征，利用深度改进模块从通道和空间注意力的角度提取到有用的深度特征信息。利用特征分组监督融合模块，依据卷积神经网络的理论，对RGB和深度特征从高层到底层分组进行多尺度多模态特征融合，每组融合加入上层融合结果和真值图进行监督，最终迭代得到预测显著图。通过4个具有代表性数据集上进行的实验，对比目前先进的RGB-D图像显著性检测，表明此模型平均绝对误差指标最小，在F值、E值和S值指标上均有提高，性能优于其他模型，具有良好的鲁棒性。
Abstract:Abstract:Aiming at the problem that the existing RGB-D image saliency detection technology is difficult to fully explore the effective information of depth image and can not effectively integrate RGB features and deep features, an RGB-D image saliency detection method under multi-branch backbone supervision network is proposed. We obtain the layer features of RGB image and deep image based on Resnet50 network, using the deep improvement module, useful deep feature information is extracted from the perspective of channel and spatial attention. Using the feature grouping supervised fusion module, according to the theory of convolutional neural network, the RGB and deep features are grouped from high level to bottom for multi-scale and multi-modal feature fusion. Each fusion group is supervised by the upper level fusion result and truth map, and finally the predicted saliency map is obtained iteratively. Experiments on four representative data sets show that compared with the current advanced RGB-D image saliency detection model. This model has the smallest average absolute error index, improves in F value,E value and S value, has better performance than other models, and has good robustness.

PDF全文下载地址:

可免费Download/下载PDF全文