计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (10): 2395-2404.DOI: 10.3778/j.issn.1673-9418.2104104
收稿日期:
2021-04-20
修回日期:
2021-06-07
出版日期:
2022-10-01
发布日期:
2021-06-11
通讯作者:
+ E-mail: xie_linbo@jiangnan.edu.cn作者简介:
李春标(1997—),男,山东聊城人,硕士研究生,主要研究方向为显著性目标检测、深度学习。基金资助:
LI Chunbiao, XIE Linbo+(), PENG Li
Received:
2021-04-20
Revised:
2021-06-07
Online:
2022-10-01
Published:
2021-06-11
About author:
LI Chunbiao, born in 1997, M.S. candidate. His research interests include salient object detection and deep learning.Supported by:
摘要:
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。
中图分类号:
李春标, 谢林柏, 彭力. 特征混合增强与多损失融合的显著性目标检测[J]. 计算机科学与探索, 2022, 16(10): 2395-2404.
LI Chunbiao, XIE Linbo, PENG Li. Salient Object Detection with Feature Hybrid Enhancement and Multi-loss Fusion[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(10): 2395-2404.
Configurations | | MAE |
---|---|---|
Baseline U-Net | 0.742 | 0.089 |
CAPM | 0.784 | 0.068 |
CAPM+FHEM(CA) | 0.773 | 0.072 |
CAPM+FHEM(ECA) | 0.803 | 0.057 |
表1 算法使用不同模块性能比较
Table 1 Performance comparison of different modules in algorithm
Configurations | | MAE |
---|---|---|
Baseline U-Net | 0.742 | 0.089 |
CAPM | 0.784 | 0.068 |
CAPM+FHEM(CA) | 0.773 | 0.072 |
CAPM+FHEM(ECA) | 0.803 | 0.057 |
Configurations | | MAE |
---|---|---|
CAPM+FHEM+ | 0.789 | 0.069 |
CAPM+FHEM+ | 0.787 | 0.064 |
CAPM+FHEM+ | 0.792 | 0.066 |
CAPM+FHEM+ | 0.803 | 0.057 |
表2 算法使用不同损失的性能比较
Table 2 Performance comparison of different losses in algorithm
Configurations | | MAE |
---|---|---|
CAPM+FHEM+ | 0.789 | 0.069 |
CAPM+FHEM+ | 0.787 | 0.064 |
CAPM+FHEM+ | 0.792 | 0.066 |
CAPM+FHEM+ | 0.803 | 0.057 |
| | | | MAE |
---|---|---|---|---|
1.0 | 1.0 | 1.0 | 0.792 | 0.066 |
1.0 | 1.0 | 2.0 | 0.797 | 0.063 |
0.1 | 0.9 | 2.0 | 0.756 | 0.076 |
0.2 | 0.8 | 2.0 | 0.762 | 0.073 |
0.3 | 0.7 | 2.0 | 0.803 | 0.057 |
0.4 | 0.6 | 2.0 | 0.801 | 0.058 |
0.5 | 0.5 | 2.0 | 0.796 | 0.061 |
表3 算法使用不同系数RA损失的性能比较
Table 3 Performance comparison of RA loss with different coefficients in algorithm
| | | | MAE |
---|---|---|---|---|
1.0 | 1.0 | 1.0 | 0.792 | 0.066 |
1.0 | 1.0 | 2.0 | 0.797 | 0.063 |
0.1 | 0.9 | 2.0 | 0.756 | 0.076 |
0.2 | 0.8 | 2.0 | 0.762 | 0.073 |
0.3 | 0.7 | 2.0 | 0.803 | 0.057 |
0.4 | 0.6 | 2.0 | 0.801 | 0.058 |
0.5 | 0.5 | 2.0 | 0.796 | 0.061 |
Model | ECSSD | DUT-OMRON | DUTS-TE | HKU-IS | SOD | |||||
---|---|---|---|---|---|---|---|---|---|---|
| MAE | | MAE | | MAE | | MAE | | MAE | |
Ours | 0.942 | 0.036 | 0.803 | 0.057 | 0.860 | 0.046 | 0.928 | 0.031 | 0.857 | 0.102 |
CapsNet | 0.887 | 0.052 | 0.703 | 0.063 | 0.799 | 0.048 | 0.880 | 0.039 | — | — |
RAS | 0.921 | 0.056 | 0.786 | 0.062 | 0.831 | 0.059 | 0.913 | 0.045 | 0.850 | 0.124 |
Amulet | 0.915 | 0.059 | 0.743 | 0.098 | 0.778 | 0.084 | 0.897 | 0.051 | 0.806 | 0.141 |
NLDF | 0.905 | 0.063 | 0.753 | 0.080 | 0.812 | 0.066 | 0.902 | 0.048 | 0.841 | 0.124 |
UCF | 0.911 | 0.078 | 0.734 | 0.132 | 0.771 | 0.117 | 0.886 | 0.074 | 0.803 | 0.164 |
RFCN | 0.890 | 0.107 | 0.742 | 0.111 | 0.784 | 0.091 | 0.892 | 0.079 | 0.799 | 0.170 |
ELD | 0.867 | 0.079 | 0.715 | 0.092 | 0.738 | 0.093 | 0.839 | 0.074 | 0.764 | 0.155 |
DCL | 0.890 | 0.143 | 0.739 | 0.097 | 0.782 | 0.088 | 0.885 | 0.072 | 0.823 | 0.141 |
LEGS | 0.827 | 0.118 | 0.669 | 0.133 | 0.655 | 0.138 | 0.766 | 0.119 | 0.734 | 0.196 |
表4 在5个数据集上不同算法性能比较
Table 4 Performance comparison of different algorithms on 5 datasets
Model | ECSSD | DUT-OMRON | DUTS-TE | HKU-IS | SOD | |||||
---|---|---|---|---|---|---|---|---|---|---|
| MAE | | MAE | | MAE | | MAE | | MAE | |
Ours | 0.942 | 0.036 | 0.803 | 0.057 | 0.860 | 0.046 | 0.928 | 0.031 | 0.857 | 0.102 |
CapsNet | 0.887 | 0.052 | 0.703 | 0.063 | 0.799 | 0.048 | 0.880 | 0.039 | — | — |
RAS | 0.921 | 0.056 | 0.786 | 0.062 | 0.831 | 0.059 | 0.913 | 0.045 | 0.850 | 0.124 |
Amulet | 0.915 | 0.059 | 0.743 | 0.098 | 0.778 | 0.084 | 0.897 | 0.051 | 0.806 | 0.141 |
NLDF | 0.905 | 0.063 | 0.753 | 0.080 | 0.812 | 0.066 | 0.902 | 0.048 | 0.841 | 0.124 |
UCF | 0.911 | 0.078 | 0.734 | 0.132 | 0.771 | 0.117 | 0.886 | 0.074 | 0.803 | 0.164 |
RFCN | 0.890 | 0.107 | 0.742 | 0.111 | 0.784 | 0.091 | 0.892 | 0.079 | 0.799 | 0.170 |
ELD | 0.867 | 0.079 | 0.715 | 0.092 | 0.738 | 0.093 | 0.839 | 0.074 | 0.764 | 0.155 |
DCL | 0.890 | 0.143 | 0.739 | 0.097 | 0.782 | 0.088 | 0.885 | 0.072 | 0.823 | 0.141 |
LEGS | 0.827 | 0.118 | 0.669 | 0.133 | 0.655 | 0.138 | 0.766 | 0.119 | 0.734 | 0.196 |
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