特征混合增强与多损失融合的显著性目标检测

Salient Object Detection with Feature Hybrid Enhancement and Multi-loss Fusion

针对当前显著性目标检测算法存在的特征缺失和区域一致性差的问题,基于全卷积神经网络提出一种特征混合增强与多损失融合的显著性目标检测算法。该算法包含上下文感知预测模块(CAPM)和特征混合增强模块(FHEM)。首先利用上下文感知预测模块提取图像多尺度特征信息,并且在预测模块中嵌入空间感知模块(SAM)以进一步提取图像高层语义信息,然后利用特征混合增强模块对预测模块产生的全局特征信息和细节特征信息进行有效的整合,并利用特征聚合模块(FAM)对整合的信息进行特征增强。此外,提出了一种新的区域增强损失函数(RA),并将此损失函数与已有的二进制交叉熵(BCE)损失函数、结构化相似度(SSIM)损失函数融合,以多损失融合的方式监督网络保持前景区域的完整性以及增强区域像素一致性。在五个包含多个显著性目标和复杂背景的图像数据集上对算法进行验证,实验结果表明,该算法有效地提高了复杂场景下显著性目标的检测精度,改善了显著图特征缺失与区域一致性差的问题。

To tackle the problem of missing features and poor regional consistency in existing salient object detec tion algorithms,a salient object detection network which uses feature hybrid enhancement and multi-loss fusion based on fully convolutional neural network is proposed.The network includes a context-aware prediction module(CAPM)and a feature hybrid enhancement module(FHEM).First,the context-aware prediction module is used to extract the multi-scale feature information of the image,in which the spatial-aware module(SAM)is embedded to further extract the high-level semantic information of the image.Furthermore,the feature hybrid enhancement module is used to effectively integrate the global feature information and the detailed feature information generated by the prediction module,and the integrated feature is enhanced through embedded feature aggregation module(FAM).In addition,the multi-loss fusion method is used to supervise the network,which combines the binary cross entropy(BCE)loss function,the structured similarity(SSIM)loss function and the proposed regional augmentation(RA)loss function.The network with the multi-loss fusion method can maintain the integrity of the foreground region and enhance the regional pixel consistency.The algorithm is verified on five image datasets with multiple salient objects and complex backgrounds.Experimental results demonstrate that the algorithm effectively improves the detection accuracy of saliency objects in complex scenes,and alleviates the problem of saliency map features missing and poor regional consistency.