液晶与显示, 2019, 34 (12): 1202, 网络出版: 2020-01-09   

基于改进深度残差网络的河蟹精准溯源系统

Accurate traceability system of crab based on improved deep residual network
侍国忠 1,2,*陈明 1,2张重阳 1,2
作者单位
1 上海海洋大学 信息学院, 上海 201306
2 农业部渔业信息重点实验室, 上海 201306
摘要
针对当前商品河蟹质量安全问题逐渐增多、真假阳澄湖大闸蟹难辨、深度残差网络提取特征维度大的问题, 提出一种基于改进深度残差网络的河蟹精准溯源系统。系统由养殖环节、检测环节、销售环节、溯源环节4部分组成, 养殖、检测、销售环节将河蟹的养殖、检测、销售数据保存到溯源数据库, 溯源环节通过基于改进深度残差网络的河蟹识别技术, 识别溯源数据库中是否存在待溯源河蟹, 并根据识别结果输出待溯源河蟹在养殖、检测、销售环节的数据, 最终实现每一只商品河蟹从消费者到养殖场的精准溯源追踪。实验结果表明, 改进深度残差网络的河蟹识别技术将提取的蟹壳特征向量从2 048维降至156维, 识别耗时降低了92%, 识别准确率为92.1%。
Abstract
Aiming at the problems of current commercial river crab quality safety, hard to distinguish between real and false Yangcheng Lake hairy crabs, and too large feature dimension of deep residual network extraction, a precise source tracing system of river crab based on improved deep residual network is proposed. The system consists of four parts: breed, detection, sale and traceability. In breed, detection and sale parts, the data of crab breed, detection and sale are stored in traceability database. The traceability part identifies whether there is traceable river in traceability database by identifying crab based on improved deep residual network. According to the result of identification, the data of crab breeding, testing and marketing are output. Finally, the precise traceability of each commercial crab from consumer to farm is realized. The improved crab identification technology of the deep residual network can reduced the extracted crab shell feature vector from 2 048 to 156 dimensions, the recognition time is reduced by 92%, and the recognition accuracy is 92.1%.
参考文献

[1] 彭姣, 徐正刚, 唐永成, 等.大通湖1龄中华绒螯蟹形态指标及质量参数研究[J].水生态学杂志, 2019, 40(1): 91-96.

    PENG J, XU Z G, TANG Y C, et al. Morphological attributes and quality parameters of one-year juvenile Eriocheir sinensis in Datong Lake [J]. Journal of Hydroecology, 2019, 40(1): 91-96. (in Chinese)

[2] 刘婧美, 刘萍.我国水产品质量安全现状分析及对策思考[J].河北渔业, 2014(9): 67-69.

    LIUJ M, LIU P. Analysis of the current situation of quality and safety of aquatic products in China and countermeasures [J]. Hebei Fisheries, 2014(9): 67-69. (in Chinese)

[3] 余明辉.洪湖蟹“洗澡”也变不成阳澄湖大闸蟹[J].人民法治, 2017(12): 80.

    YU M H. The "bathing" of Honghu crabs has not become a hairy crab in Yangcheng Lake [J]. People's Rule of Law, 2017(12): 80. (in Chinese)

[4] 谭莉, 李汴生.农产品与加工食品产地溯源技术研究进展[J].农产品加工(学刊), 2014(8): 81-85.

    TANL, LI B S. Advances in methods for geographical origin traceability of agricultural products and processed foods [J]. Academic Periodical of Farm Products Processing, 2014(8): 81-85. (in Chinese)

[5] 付延松, 胡更生, 王群, 等.一种基于多重信息加密的二维码防伪方法[J].印刷质量与标准化, 2014(4): 28-31.

    FU Y S, HU G S, WANG Q, et al. A two-dimensional code anti-counterfeiting method based on multiple information encryption [J]. Printing Quality and Standardization, 2014(4): 28-31. (in Chinese)

[6] 黄颖为, 龚小超.二维条码技术及其在防伪中的应用[J].中国品牌与防伪, 2007(7): 60-63.

    HUANG Y W, GONG X C. Two-dimensional bar code technology and its application in anti-counterfeiting [J]. China Brand and Anti-Counterfeiting, 2007(7): 60-63. (in Chinese)

[7] 陆军, 董娟, 冯子慧.基于Web的中华绒螯蟹养殖质量安全信息可追溯系统研究[J].海洋渔业, 2018, 40(1): 89-96.

    LU J, DONG J, FENG Z H. Traceability system for Eriocheir sinensis breeding quality safety information based on Web [J]. Marine Fisheries, 2018, 40(1): 89-96. (in Chinese)

[8] 周兴国, 魏志强, 李臻, 等.基于移动端的水产品防伪溯源系统的实现[J].农业网络信息, 2016(9): 48-51.

    ZHOU X G, WEI Z Q, LI Z, et al. Implementation of anti-fake and tracing system of aquatic products based on mobile terminal [J]. Agriculture Network Information, 2016(9): 48-51. (in Chinese)

[9] 张炳良, 练迪, 任海良.基于蟹壳图像的中华绒螯蟹防伪体系研究[J].科学养鱼, 2014(2): 77-78.

    ZHANG B L, LIAN D, REN H L. Study on the anti-counterfeiting system of Eriocheir sinensis based on crab shell image [J]. Scientific Fish Farming, 2014(2): 77-78. (in Chinese)

[10] 曹海燕.基于几何特征的人脸识别算法研究[D].曲阜: 曲阜师范大学, 2015.

    CAO H Y. Research on face recognition algorithm based on geometric features [D]. Qufu: Qufu Normal University, 2015. (in Chinese)

[11] 何云, 吴怀宇, 钟锐.基于多种LBP特征集成学习的人脸识别[J].计算机应用研究, 2018, 35(1): 292-295.

    HE Y, WU H Y, ZHONG R. Face recognition based on ensemble learning with multiple LBP features [J]. Application Research of Computers, 2018, 35(1): 292-295. (in Chinese)

[12] TAN H L, YANG B, MA Z M. Face recognition based on the fusion of global and local HOG features of face images [J]. IET Computer Vision, 2014, 8(3): 224-234.

[13] NASEEM I, TOGNERI R, BENNAMOUN M. Linear regression for face recognition [J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(11): 2106-2112.

[14] 栗科峰, 黄全振.融合深度学习与最大间距准则的人脸识别方法[J].计算机工程与应用, 2018, 54(5): 206-210.

    LI K F, HUANG Q Z. Fusion of deep learning and maximum margin criterion for face recognition [J]. Computer Engineering and Applications, 2018, 54(5): 206-210. (in Chinese)

[15] CALDWELL T. Google FaceNet scores almost 100% recognition [J]. Biometric Technology Today, 2015, 2015(4): 2-3.

[16] 王武, 王成辉, 马旭洲.河蟹生态养殖[M].2版.北京: 中国农业出版社, 2013.

    WANG W, WANG C H, MA X Z. River Crab Ecological Farming [M]. 2nd ed. Beijing: China Agriculture Press, 2013. (in Chinese)

[17] 王成辉, 李思发.中华绒螯蟹种质研究进展[J].中国水产科学, 2002, 9(1): 82-86.

    WANG C H, LI S F. Advances in studies on germplasm in Chinese mitten crab, Erocheir sinensis [J]. Journal of Fishery Sciences of China, 2002, 9(1): 82-86. (in Chinese)

[18] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: IEEE, 2016.

[19] 张枫, 田联房, 杜启亮.基于残差网络与中心损失的人脸识别[J].计算机工程与设计, 2019, 40(6): 1689-1695.

    ZHANG F, TIAN L F, DU Q L. Face recognition based on resnet and center loss [J]. Computer Engineering and Design, 2019, 40(6): 1689-1695. (in Chinese)

[20] WEN Y D, ZHANG K P, LI Z F, et al. A discriminative feature learning approach for deep face recognition[C]//Proceedings of the 14th European Conference on Computer Vision. The Netherlands: Springer, 2016.

[21] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting [J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.

[22] ROMAN-RANGEL E, MARCHAND-MAILLET S. Inductive t-SNE via deep learning to visualize multi-label images [J]. Engineering Applications of Artificial Intelligence, 2019, 81: 336-345.

侍国忠, 陈明, 张重阳. 基于改进深度残差网络的河蟹精准溯源系统[J]. 液晶与显示, 2019, 34(12): 1202. SHI Guo-zhong, CHEN Ming, ZHANG Chong-yang. Accurate traceability system of crab based on improved deep residual network[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(12): 1202.

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