SENet optimized Deeplabv3+ freshwater fish body semantic segmentation*

doi:10.12398/j.issn.2096-7217.2021.01.005

Journal of Intelligent Agricultural Mechanization (in Chinese and English) ›› 2021, Vol. 2 ›› Issue (1): 36-43.DOI: 10.12398/j.issn.2096-7217.2021.01.005

Previous Articles Next Articles

SENet optimized Deeplabv3+ freshwater fish body semantic segmentation^*

Hongjun Wang¹, Xiaoyu Ji¹, Hui Zhao^1,2, Youjun Yue¹

1. College of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin, 300384, China;
2. College of Engineering and Technology, Tianjin Agricultural University, Tianjin, 300392, China

Received:2021-05-01 Online:2021-05-15 Published:2021-11-30
About author:Hongjun Wang, Professor, research direction: theory and application of complex system intelligent control. E-mail: hongewang@126.com;Translator:Mingxin Li, University of Illinois at Urbana-Champaign, research direction: computer engineering. E-mail: Mingxin9@illinois.edu
Supported by:
*Tianjin Science and Technology Support Plan Project (17ZXYENC00080、18YFZCNC01120、15ZXZNGX00290)

Abstract

Abstract: The rapid identification and precise positioning of the freshwater fish's head, abdomen, and fins are prerequisites for the robot to achieve fast capturing, accurate segmentation, and the core of working efficiency improvement. The freshwater fish body's semantic segmentation algorithm conducts many invalid feature channels under the encoding feature extraction stage, and the constant network sample collection and pooling operation lead to the loss of detailed information of some fish bodies, decreasing the performance and poor the performance effect of edge cutting. A semantic segmentation algorithm base on SENet optimized Deeplabv3+ freshwater fish head, abdomen, and fins is proposed. Using dilated/atrous convolutions to enlarge the receptive field, prevent detail information loss, and actualize the precise positioning. Meanwhile, the SENet optimization enables Deeplabv3+ to improve the useful feature of freshwater fish and restrains its ineffectual feature. Eventually, the semantic segmentation MIoU of each part of the freshwater fish reached about 93% on the self-established freshwater fish data set. Its feature improved significantly and reached the current advanced segmentation level.

Key words: recognition, location, deep learning, characteristic channel, receptive field, semantic segmentation

CLC Number:

S24: TP391.4

Hongjun Wang, Xiaoyu Ji, Hui Zhao, Youjun Yue. SENet optimized Deeplabv3+ freshwater fish body semantic segmentation^*[J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2021, 2(1): 36-43.

References

[1] Storbeck F, Daan B. Fish species recognition using computer vision and neural network [J]. Fisheries Research, 2001, 51(1): 11-15
[2] 付中正, 何潇, 方逵, 等. 基于改进SSD网络的西兰花叶片检测研究[J]. 中国农机化学报, 2020, 41(4): 92-97.
Fu Zhongzheng, He Xiao, Fang Kui, at al. Study on the detection of broccoli leaves based on the improved SSD networks [J]. Journal of Chinese Agricultural Mechanization, 2020, 41(4): 92-97.
[3] Otsu N. A threshold selection method from gray-level histograms [J]. Systems Man & Cybernetics IEEE Transactions on, 1979, 9(1): 62-66.
[4] Canny J. A computational approach to edge detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[5] 杨杰超, 许江淳, 陆万荣, 等. 基于计算机视觉的大黄鱼体尺测算与体质量估测[J]. 中国农机化学报, 2018, 39(6): 66-70.
Yang Jiechao, Xu Jiangchun, Lu Wanrong, at al. Computer vision-based body size measurement and weight estimation of large yellow croaker [J]. Journal of Chinese Agricultural Mechanization, 2018, 39(6): 66-70.
[6] 张军文, 陈庆余, 沈建, 等. 开背大黄鱼去脏用鱼体仿形料槽设计研究[J]. 中国农机化学报, 2019, 40(7): 63-67.
Zhang Junwen, Chen Qingyu, Shen Jian, at al. Design and research of shape-like feeding tank for scratching Pseudosciaena crocea [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(7): 63-67.
[7] Shi G, Hu J, Da L, et al. Dynamic artificial neural networks based on the target featuer and aplication in target recognition [C]. Conferece on Robotics and Biomimetics, New york: IEEE, 2007: 2106-2109.
[8] 王聃, 柴秀娟. 机器学习在植物病害识别研究中的应用[J]. 中国农机化学报, 2019, 40(9): 171-180.
Wang Dan, Chai Xiujuan. Application of machine learning in plant diseases recognition [J]. Journal of Chinese Agricultural Mechanization, 2019, 40(9): 171-180.
[9] Hu J, Shen L, Sun G. Squeeze-and excitation networks [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[10] Chen L C, Zhu Y K, Papandreou G, at al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]. Proceedings of the European Conference on Computer Vision, 2018: 801-818.
[11] He K, Zhang X, Ren S, at al. Spatial pyramid pooling in deep convolutional networks for visual recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[12] Long J,Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [J]. IEEE Conference on Computer Vision and Pattern Recognition,2015, 5(2): 3431-3440.
[13] Zhou X, Yang G. Normalization in training U-NET for 2D biomendical semantic segmentation [J]. IEEE Robotics and Automation Letters, 2019, 4(2): 1792-1799.
[14] Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs [J]. Computer Science, 2014, 4(6): 357-361.
[15] Chen L C, Papandreou G, Kokkinos I, at al. DeepLab: semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs [J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2018, 40(4): 834-848.
[16] Yurtkulu S C, Sahin Y H, Unal G. Semantic segmentation with extended deeplabv3 architecture [C]. Signal Processing and Communications Applications Conference, 2019: 1-4.
[17] 张潜, 桑军, 吴伟群, 等. 基于Xception的细粒度图像分类[J]. 重庆大学学报, 2018, 41(5): 85-91.
Zhang Qian, Sang Jun, Wu Weiqun, at al. Fine-grained image classification based on Xception [J]. Journal of Chongqing University, 2018, 41(5): 85-91.
[18] 张云, 王志伟, 史鹏坤, 等. 基于机器视觉的棉花异性纤维检测技术优化研究[J]. 中国农机化学报, 2018, 39(9): 61-65.
Zhang Yun, Wang Zhiwei, Shi Pengkun, at al. Optimization of cotton heterosexual detection technology based on machine vision [J]. Journal of Chinese Agricultural Mechanization, 2018, 39(9): 61-65.

SENet optimized Deeplabv3+ freshwater fish body semantic segmentation^*

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 4

Recommended Articles

Metrics

Comments

[1]	Yan Kai, Gao Yue, Dai Baisheng, Sun Hongmin, Yin Yanling, Shen Weizheng. Research on detection of aggressive behavior among gregarious pigs based on anchor-free temporal localization [J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 17-25.
[2]	Tang Yurong, Shen Mingxia, Xue Hongxiang, Chen Jinxin. Development status and prospect of artificial intelligence technology in livestock and poultry breeding [J]. Journal of Intelligent Agricultural Mechanization, 2023, 4(1): 1-16.
[3]	Guowei Wang, Jiaxin Liu. Research on corn disease recognition method based on convolutional neural network^* [J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2021, 2(1): 64-70.
[4]	Tingting Mao, Shuxian Dong, Jinlin Xue. Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation Dynamic function allocation of agricultural robot vehicle controlled by man-machine cooperation^* [J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2020, 1(1): 24-31.