Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (10): 3177-3183.DOI: 10.11772/j.issn.1001-9081.2021091614
• Multimedia computing and computer simulation • Previous Articles Next Articles
Qiwen WU, Jianhua WANG, Xiang ZHENG, Ju FENG, Hongyan JIANG, Yubo WANG
Received:
2021-09-13
Revised:
2021-12-24
Accepted:
2022-01-04
Online:
2022-04-15
Published:
2022-10-10
Contact:
Jianhua WANG
About author:
WU Qiwen, born in 1997, M. S. candidate. Her research interests include machine vision.Supported by:
吴奇文, 王建华, 郑翔, 冯居, 姜洪岩, 王昱博
通讯作者:
王建华
作者简介:
第一联系人:吴奇文(1997—),女,江西上饶人,硕士研究生,主要研究方向:机器视觉基金资助:
CLC Number:
Qiwen WU, Jianhua WANG, Xiang ZHENG, Ju FENG, Hongyan JIANG, Yubo WANG. Waterweed image segmentation method based on improved U-Net[J]. Journal of Computer Applications, 2022, 42(10): 3177-3183.
吴奇文, 王建华, 郑翔, 冯居, 姜洪岩, 王昱博. 基于改进U-Net的水草图像分割方法[J]. 《计算机应用》唯一官方网站, 2022, 42(10): 3177-3183.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021091614
实际标签 | 预测标签 | |
---|---|---|
是水草 | 不是水草 | |
是水草 | 真正TP(True Positive) | 假正FP(False Positive) |
不是水草 | 假负FN(False Negative) | 真负TN(True Negative) |
Tab. 1 Evaluation index basis
实际标签 | 预测标签 | |
---|---|---|
是水草 | 不是水草 | |
是水草 | 真正TP(True Positive) | 假正FP(False Positive) |
不是水草 | 假负FN(False Negative) | 真负TN(True Negative) |
方法 | 准确率 | mIoU | mPA |
---|---|---|---|
FCN(VGG16)[ | 89.22 | 75.98 | 86.90 |
SegNet[ | 90.78 | 79.80 | 88.12 |
U-Net(VGG16)[ | 92.18 | 87.35 | 92.17 |
U-Net(ResNet18)[ | 93.52 | 89.42 | 94.27 |
DeepLabv3[ | 95.93 | 89.96 | 94.86 |
本文方法 | 96.80 | 91.22 | 95.29 |
Tab. 2 Comparison of segmentation results of different methods
方法 | 准确率 | mIoU | mPA |
---|---|---|---|
FCN(VGG16)[ | 89.22 | 75.98 | 86.90 |
SegNet[ | 90.78 | 79.80 | 88.12 |
U-Net(VGG16)[ | 92.18 | 87.35 | 92.17 |
U-Net(ResNet18)[ | 93.52 | 89.42 | 94.27 |
DeepLabv3[ | 95.93 | 89.96 | 94.86 |
本文方法 | 96.80 | 91.22 | 95.29 |
方法 | 准确率 | mIoU | mPA |
---|---|---|---|
U-Net(ResNet18) | 93.52 | 89.42 | 94.27 |
+混合注意力模块 | 95.89 | 90.05 | 94.71 |
+多尺度图像输入 | 95.79 | 89.45 | 94.29 |
+多尺度图像输入+混合损失函数 | 96.13 | 90.66 | 94.67 |
本文方法 | 96.80 | 91.22 | 95.29 |
Tab. 3 Comparison of ablation experimental results on waterweed dataset
方法 | 准确率 | mIoU | mPA |
---|---|---|---|
U-Net(ResNet18) | 93.52 | 89.42 | 94.27 |
+混合注意力模块 | 95.89 | 90.05 | 94.71 |
+多尺度图像输入 | 95.79 | 89.45 | 94.29 |
+多尺度图像输入+混合损失函数 | 96.13 | 90.66 | 94.67 |
本文方法 | 96.80 | 91.22 | 95.29 |
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