计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 103-109.doi: 10.11896/jsjkx.200600068
潘明远, 宋慧慧, 张开华, 刘青山
PAN Ming-yuan, SONG Hui-hui, ZHANG Kai-hua, LIU Qing-shan
摘要： 针对目前显著性目标检测算法中存在的特征融合不充分、模型较为冗余等问题,提出了一种基于全局引导渐进特征融合的轻量级显著性目标检测算法。首先,使用轻量特征提取网络MobileNetV3对图像提取不同层次的多尺度特征;然后对MobileNetV3提取的高层语义特征使用轻量级多尺度感受野增强模块以进一步增强其全局特征的表征力;最后设计渐进特征融合模块对多层多尺度特征自顶而下逐步融合,并采用常用的交叉熵损失函数在多个阶段对这些融合特征进行优化,得到由粗到细的显著图。整个网络模型是无需预处理和后处理的端到端结构。在6个基准数据集上进行了大量实验,并采用PR_Curve,F-measure,S-measure和MAE指标来衡量性能。结果表明,所提方法明显优于10种先进的对比方法,并且算法模型大小仅约为10MB,在GTX2080Ti显卡上处理大小为400×300像素的图像的速度可以达到46帧/秒。
中图分类号:
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