电子学报 ›› 2022, Vol. 50 ›› Issue (2): 461-469.DOI: 10.12263/DZXB.20201416
蔡超丽1, 李纯纯2, 黄琳1, 杨铁军1
收稿日期:
2020-12-11
修回日期:
2021-10-19
出版日期:
2022-02-25
通讯作者:
作者简介:
基金资助:
CAI Chao-li1, LI Chun-chun2, HUANG Lin1, YANG Tie-jun1
Received:
2020-12-11
Revised:
2021-10-19
Online:
2022-02-25
Published:
2022-02-25
Corresponding author:
Supported by:
摘要:
为了提高深度卷积神经网络(Convolutional Neural Network,CNN)设计的自动化程度并进一步提高陶瓷晶粒扫描电子显微镜(Scanning Electron Microscope,SEM)图像分割的准确性,提出了一种基于神经网络架构搜索的陶瓷晶粒图像分割方法.该方法设计多分支结构编码空间和链式结构解码空间,并构造多分支结构编码Cell和链式结构解码Cell;同时基于强化学习分别搜索最佳编码Cell和解码Cell;此外,基于编码-解码神经网络架构堆叠最佳Cell构建陶瓷晶粒图像分割CNN,并采用池化索引在解码阶段恢复丢失的细节信息.实验在包含了629张的陶瓷晶粒SEM图像数据集上进行,搜索最佳Cell耗时约148 GPU-时.与U-Net、SegNet等SOTA方法相比,该方法在陶瓷晶粒测试集上获得了更高的分割准确性(mIoU
中图分类号:
蔡超丽, 李纯纯, 黄琳, 等. ED-NAS: 基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法[J]. 电子学报, 2022, 50(2): 461-469.
Chao-li CAI, Chun-chun LI, Lin HUANG, et al. ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images[J]. Acta Electronica Sinica, 2022, 50(2): 461-469.
操作 | 操作码 |
---|---|
3×3 dw conv | 0 |
5×5 dw conv | 1 |
3×3 conv | 2 |
3×3 dl conv,dilation rate=2 | 3 |
3×3 max pooling | 4 |
表1 候选操作集合
操作 | 操作码 |
---|---|
3×3 dw conv | 0 |
5×5 dw conv | 1 |
3×3 conv | 2 |
3×3 dl conv,dilation rate=2 | 3 |
3×3 max pooling | 4 |
类别 | 训练集 | 验证集 | 测试集 | 总数 | 标签 |
---|---|---|---|---|---|
板条状 | 422 | 60 | 119 | 601 | 1 |
圆柱状 | 435 | 63 | 125 | 623 | 2 |
表2 数据集数量分布及标签
类别 | 训练集 | 验证集 | 测试集 | 总数 | 标签 |
---|---|---|---|---|---|
板条状 | 422 | 60 | 119 | 601 | 1 |
圆柱状 | 435 | 63 | 125 | 623 | 2 |
候选网络 | 分割精度(mIoU)/% |
---|---|
N=3,L=4,链式结构D-cell | 54.9 |
N=4,L=3,链式结构D-cell | 56.6 |
N=3,L=4,多分支结构D-cell | 51.3 |
N=4,L=3,多分支结构D-cell | 53.5 |
表3 候选网络最佳验证准确性比较
候选网络 | 分割精度(mIoU)/% |
---|---|
N=3,L=4,链式结构D-cell | 54.9 |
N=4,L=3,链式结构D-cell | 56.6 |
N=3,L=4,多分支结构D-cell | 51.3 |
N=4,L=3,多分支结构D-cell | 53.5 |
方法 | 分割精度(mIoU)/% |
---|---|
40.4 | |
47.7 | |
48.8 | |
58.1 | |
59.1 | |
68.0 | |
68.9 |
表4 不同方法在陶瓷晶粒数据集上的分割准确性
方法 | 分割精度(mIoU)/% |
---|---|
40.4 | |
47.7 | |
48.8 | |
58.1 | |
59.1 | |
68.0 | |
68.9 |
1 | 张伟儒, 李伶, 王坤. 先进陶瓷材料研究现状及发展趋势[J]. 新材料产业, 2016, (1): 2-8. |
ZHANGWeiru, LILing, WANGKun. Research status and development trend of advanced ceramic materials[J]. New Materials Industry, 2016, (1): 2-8. (in Chinese) | |
2 | CHENY W, MOUSSIJ, DRURYJ L, et al. Zirconia in biomedical applications[J]. Expert Rev Med Devices, 2016: 945-963. |
3 | VIOLAG, CHONGK B, ERIKSSONM, et al. Effect of grain size on domain structures, dielectric and thermal depoling of Nd-substituted bismuth titanate ceramics[J]. Applied Physics Letters, 2013, 103(18): 899. |
4 | 雷涛, 李云彤, 周文政, 等. 数据与模型联合驱动的陶瓷材料晶粒分割[J]. 自动化学报, 2020, 46(x): 1-16. DOI:10.16383/j.aas.c200277. |
LEITao, LIYuntong, ZHOUWenzheng, et al. Grain segmentation of ceramic materials using data-driven jointing model-driven[J]. Acta Automatica Sinica, 2020, 46(x): 1-16. DOI:10.16383/j.aas.c200277. (in Chinese) | |
5 | BODNARJ L, NICOLASJ L, CANDORÉJ C. Non-destructive testing by infrared thermography under random excitation and ARMA analysis[J]. International Journal of Thermophysics, 2012, 33(10-11): 2011-2015. |
6 | ZHANGL X, XUZ, WEIS, et al. Grain size automatic determination for 7050 Al alloy based on a fuzzy logic method[J]. Rare Metal Materials and Engineering, 2016, 45: 548-554. |
7 | LIQ, NIX, LIUG. Ceramic image processing using the second curvelet transform and watershed algorithm[C]//IEEE International Conference on Robotics and Biomimetics(ROBIO). Sanya, China: IEEE, 2007: 2037-2042. |
8 | VINCENTL, SOILLEP. Watersheds in digital spaces: An efficient algorithm based on immersion simulations[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(6): 583-598. |
9 | HEILBRONNERR. Automatic grain boundary detection and grain size analysis using polarization micrographs or orientation images[J]. Journal of Structural Geology, 2000, 22: 969-981. |
10 | SCHMIDHUBERJ. Deep learning in neural networks: An overview[J]. Neural networks, 2015, 61: 85-117. |
11 | RONNEBERGERO, FISCHERP, BROXT. U-Net: Convolutional networks for biomedicalimage segmentation[EB/OL]. (2015-05-18). . |
12 | LONGJ, SHELHAMERE, DARRELLT. Fully convolutional networks for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Boston, MA, USA: IEEE, 2015: 3431-3440. |
13 | JIANGF, GUQ, HAOH, et al. Feature extraction and grain segmentation of sandstone images based on convolutional neural networks[C]//24th International Conference on Pattern Recognition(ICPR). Beijing, China: IEEE, 2018: 2636-2641. |
14 | MAB, LIUC, WEIX, et al. WPU-Net: Boundary learning by usi-ng weighted propagation in convolution network[EB/OL]. (2019-05-22). . |
15 | LIUY, CHENGMM, HUX, et al. Richer convolutional features for edge detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 3000-3009. |
16 | ZOPHB, LEQ V. Neural architecture search with reinforcement learning[EB/OL]. 78, 2017-02-15. |
17 | LIUC, CHENLC, SCHROFFF, et al. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach, CA, USA: IEEE, 2019: 82-92. |
18 | ZHANGY, QIUZ, LIUJ, et al. Customizable Architecture Search for Semantic Segmentation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach, CA, USA: IEEE, 2019: 11633-11642. |
19 | ELSKENT, METZENJ H, HUTTERF. Neural architecture search: A survey[EB/OL]. (2019-04-26). . |
20 | ZHONGZ, YANJ, WUW, et al. Practical block-wise neural network architecture generation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 2423-2432. |
21 | ZOPHB, VASUDEVANV, SHLENSJ, et al. Learning transferable architectures for scalable image recognition[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 8697-8710. |
22 | CHENLC, ZHUY, PAPANDREOUG, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision(ECCV). Munich: Springer, 2018: 801-818. |
23 | ELSKENT, METZENJ H, HUTTERF. Simple and efficient architecture search for convolutional neural networks[EB/OL]. (2017-11-13). . |
24 | BAKERB, GUPTAO, NAIKN, et al. Designing neural network architectures using reinforcement learning[EB/OL]. (2017-03-22). . |
25 | HEK, ZHANGX, RENS, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Seattle, WA, USA: IEEE, 2016: 770-778. |
26 | REALE, AGGARWALA, HUANGY, et al. Regularized evolution for image classifier architecture search[EB/OL]. (2019-02-16). . |
27 | LIUC, ZOPHB, SHLENSJ, et al. Progressive neural architecture search[EB/OL]. (2018-07-26). . |
28 | NOH H, HONGS, HANB. Learning deconvolution network for semantic segmentation[C]//IEEE International Conference on Computer Vision(ICCV). Santiago, Chile: IEEE, 2015: 1520-1528. |
29 | BADRINARAYANANV, KENDALLA, CIPOLLAR. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39: 2481-2495. |
30 | PHAMH, GUANM Y, ZOPHB, et al. Efficient neural architecture search via parameter sharing[EB/OL]. (2018-02-12). |
31 | ZHAOH, SHIJ, QIX, et al. Pyramid scene parsing network[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, HI, USA: IEEE, 2017: 6230-6239. |
32 | ZAREMBAW, SUTSKEVERI, VINYALSO. Recurrent neural network regularization[EB/OL]. (2015-02-19). . |
33 | SUTTONR S, MCALLESTERD, SINGHS, et al. Policy gradient methods for reinforcement learning with function approximation[J]. Advances in Neural Information Processing Systems, 1999, 12: 1057-1063. |
34 | ROMERAE, ALVAREZJ M, BERGASAL M, et al. ERFNet: Efficient residual factorized convnet for real-time semantic segmentation[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(1): 263-272. |
35 | PASZKEA, CHAURASIAA, KIMS, et al. ENet: A deep neural network architecture for real-time semantic segmentation[EB/OL].(2016-06-07). . |
36 | CHENLC, PAPANDREOUG, SCHROFFF, et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. (2017-12-05). . |
[1] | 唐利明, 熊点华, 方壮. 基于比尔朗伯定律的变分水平集模型[J]. 电子学报, 2023, 51(2): 416-426. |
[2] | 金天虎, 陶砚蕴, 李佐勇. 基于超像素图像分割的暗通道先验去雾改进算法[J]. 电子学报, 2023, 51(1): 146-159. |
[3] | 张淑军, 彭中, 李辉. SAU-Net:基于U-Net和自注意力机制的医学图像分割方法[J]. 电子学报, 2022, 50(10): 2433-2442. |
[4] | 吴绿, 张馨月, 唐茉, 王梓, 王永安. Focus+Context语义表征的场景图像分割[J]. 电子学报, 2021, 49(3): 596-604. |
[5] | 朱占龙, 刘永军. 融合混沌优化和改进模糊聚类的图像分割算法[J]. 电子学报, 2020, 48(5): 975-984. |
[6] | 梁新宇, 林洗坤, 权冀川, 肖铠鸿. 基于深度学习的图像实例分割技术研究进展[J]. 电子学报, 2020, 48(12): 2476-2486. |
[7] | 石雪, 李玉, 赵泉华. 自适应类别的层次高斯混合模型遥感影像分割[J]. 电子学报, 2020, 48(1): 131-136. |
[8] | 雷涛, 张肖, 加小红, 刘侍刚, 张艳宁. 基于模糊聚类的图像分割研究进展[J]. 电子学报, 2019, 47(8): 1776-1791. |
[9] | 侯小刚, 赵海英, 马严. 基于超像素多特征融合的快速图像分割算法[J]. 电子学报, 2019, 47(10): 2126-2133. |
[10] | 范虹, 张程程, 侯存存, 朱艳春, 姚若侠. 结合双树复小波变换和改进密度峰值快速搜索聚类的乳腺MR图像分割[J]. 电子学报, 2019, 47(10): 2149-2157. |
[11] | 张峰, 钟宝江. 基于兴趣目标的图像检索[J]. 电子学报, 2018, 46(8): 1915-1923. |
[12] | 李亚峰. 一种基于多字典学习的图像分割模糊方法[J]. 电子学报, 2018, 46(7): 1700-1709. |
[13] | 李磊, 董卓莉, 张德贤. 基于自适应区域限制FCM的图像分割方法[J]. 电子学报, 2018, 46(6): 1312-1318. |
[14] | 周则明, 胡彪, 孟勇, 陈超迁, 罗其祥. 基于流形特征与形状先验的红外直升机图像分割[J]. 电子学报, 2018, 46(4): 834-839. |
[15] | 王慧斌, 高国伟, 徐立中, 文成林. 基于纹理特征的多区域水平集图像分割方法[J]. 电子学报, 2018, 46(11): 2588-2596. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||