Volume 20 Issue 1
Feb.  2024
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WEI Tianqi, ZHENG Xiongsheng, LI Tianbing, WANG Richeng. Multi-group convolutional neural network for gender recognition of Portunus tritubereulatus[J]. South China Fisheries Science, 2024, 20(1): 89-98. DOI: 10.12131/20230107
Citation: WEI Tianqi, ZHENG Xiongsheng, LI Tianbing, WANG Richeng. Multi-group convolutional neural network for gender recognition of Portunus tritubereulatus[J]. South China Fisheries Science, 2024, 20(1): 89-98. DOI: 10.12131/20230107
shu

Multi-group convolutional neural network for gender recognition of Portunus tritubereulatus

  • 1.

    School of Marine Engineering Equipment, Zhejiang Ocean University, Zhoushan 316002, China

  • 2.

    City University of Hefei, Hefei 231131, China

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  • Received Date: May 19, 2023
  • Revised Date: July 30, 2023
  • Accepted Date: September 08, 2023
  • Available Online: September 12, 2023
    Graphical Abstract
    • Abstract

  • High-precision intelligent recognition and classification has become a key technology for intelligent sorting of Portunus trituberculatus. We first preprocessed and enhanced the collected images of P. tritubereulatus so as to build a Portunus gender classification dataset (PGCD). Besides, we proposed a multi-group convolutional neural network for gender classification of P. tritubereulatus, mainly using ResNet50 to extract features from image patches, thereby reducing information loss during the feature extraction process. In order to focus more on finding useful information of input data, we also constructed an attention mechanism before gender classification to emphasize the importance of details in the global feature map. The results show that the classification accuracy, recall and accuracy of this method on PGCD were 95.59%, 94.41% and 96.68%, respectively, with a recognition error rate of only 4.41%. It is concluded that the method has superior classification performance and can be used in automatic classification and recognition systems for Portunus gender.

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    • References (46)
  • [1]
    王陈星, 倪震宇, 刘必林, 等. 利用胃磨鉴定三疣梭子蟹年龄初探[J]. 南方水产科学, 2020, 16(3): 94-102.
    [2]
    DIXIT M, RASIWASIA N, VASCONCELOS N. Adapted gaussian models for image classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA. IEEE, 2011: 937-943.
    [3]
    GAO Z H, LIU X W. Support vector machine and object-oriented classification for urban impervious surface extraction from satellite imagery[C]//The Third International Conference on Agro-Geoinformatics (ICAG), Beijing, China. IEEE, 2014: 1-5.
    [4]
    GAN Z, ZHONG L, LI Y F, et al. A random forest based method for urban object classification using lidar data and aerial imagery[C]//The 23rd International Conference on Geoinformatics. Wuhan, China. IEEE, 2015: 1-4.
    [5]
    MUNOZ D, BAGNELL J A, VANDAPEL N, et al. Contextual classification with functional max-margin markov networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA. IEEE, 2009: 975-982.
    [6]
    NIEMEYER J, ROTTENSTEINER F, SOERGEL U. Contextual classification of lidar data and building object detection in urban areas[J]. Isprs J Photogramm, 2014, 87: 152-165. doi: 10.1016/j.isprsjprs.2013.11.001
    [7]
    LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. P IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791
    [8]
    LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
    [9]
    KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Commun Acm, 2017, 60(6): 84-90. doi: 10.1145/3065386
    [10]
    SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2022-02-26]. https://arxiv.org/abs/1409.1556.pdf.
    [11]
    SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA. IEEE, 2015: 1-9.
    [12]
    HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. IEEE, 2016: 770-778.
    [13]
    HUANG G, LIU Z, MAATEN L V D, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. IEEE, 2017: 4700-4708.
    [14]
    KRIZHEVSKY A, HINTON G. Learning multiple layers of features from tiny images[J]. Tech Rep, 2009(4): 1-80.
    [15]
    RUSSAKOVSKY O, DENG J, SU H, et al. Imagenet large scale visual recognition challenge[J]. Int J Comput Vision, 2015, 115(3): 211-252. doi: 10.1007/s11263-015-0816-y
    [16]
    杜丽君, 唐玺璐, 周娇, 等. 基于注意力机制和多任务学习的阿尔茨海默症分类 [J]. 计算机科学, 2022, 49(S1): 60-65.
    [17]
    王宁, 程家骅, 张寒野, 等. 水产养殖水体遥感动态监测及其应用 [J]. 中国水产科学, 2019, 26(5): 893-903.
    [18]
    朱明, 张镇府, 黄凰, 等. 基于轻量级神经网络MobileNetV3-Small的鲈鱼摄食状态分类 [J]. 农业工程学报, 2021, 37(19): 165-172.
    [19]
    CAI J Y, LI C L, TAO X, et al. Image multi-inpainting via progressive generative adversarial networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA. IEEE, 2022: 978-987.
    [20]
    LIU F B, TIAN Y, CHEN Y H, et al. ACPL: anti-curriculum pseudo-labelling for semi-supervised medical image classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA. IEEE, 2022: 20697-20706.
    [21]
    MAITY A, ROY S G, BHATTACHARJEE S, et al. Prediction of Indian currency for visually impaired people using machine learning[M]. Singapore: Springer Nature Singapore, 2022: 263-275.
    [22]
    YU H L, CHENG X H, CHEN C C, et al. Apple leaf disease recognition method with improved residual network[J]. Multimed Tools Appl, 2022, 81(6): 7759-7782. doi: 10.1007/s11042-022-11915-2
    [23]
    WEI K M, LI T Q, HUANG F R, et al. Cancer classification with data augmentation based on generative adversarial networks[J]. Front Comput Sci, 2022, 16(2): 1-11.
    [24]
    ZHAO Y, SHI Y B, WANG Z L. The improved YOLOV5 algorithm and its application in small target detection[C]//International Conference on Intelligent Robotics and Applications. Cham: Springer, 2022: 679-688.
    [25]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Adva Info Proces Sys, 2017, 30: 1-11.
    [26]
    DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale [EB/OL]. (2021-06-03)[2022-02-26]. https://arxiv.org/pdf/2010.11929.pdf.
    [27]
    HU Y Y, SUN S L, XU X, et al. Multi-view deep attention network for reinforcement learning  (student abstract)[C]//Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New York, NY, USA. 2020, 34 (10): 13811-13812.
    [28]
    WANG W J, WANG T, CAI Y. Multi-view attention-convolution pooling network for 3D point cloud classification[J]. Appl Intell, 2022, 52: 14787-14798.
    [29]
    WANG W J, CAI Y, WANG T. Multi-view dual attention network for 3D object recognition[J]. Neural Comput Appl, 2022, 34(4): 3201-3212. doi: 10.1007/s00521-021-06588-1
    [30]
    PASZKE A, GROSS S, MASSA F, et al. Pytorch: an imperative style, high-performance deep learning library[J]. Adva Informa Process, 2019, 32: 1-12.
    [31]
    ZHANG Z J. Improved adam optimizer for deep neural networks[C]//IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), Banff, AB, Canada. IEEE, 2018: 1-2.
    [32]
    CEN F, ZHAO X Y, LI W Z, et al. Deep feature augmentation for occluded image classification[J]. Pattern Recogn, 2021, 10: 1-13.
    [33]
    BIRD J J, BARNES C M, MANSO L J, et al. Fruit quality and defect image classification with conditional GAN data augmentation[J]. Sci Hortic, 2022, 10: 1-11.
    [34]
    LANG H T, WU S W. Ship classification in moderate-resolution SAR image by naive geometric features-combined multiple kernel learning[J]. IEEE Geosci Remote S, 2017, 14(10): 1765-1769. doi: 10.1109/LGRS.2017.2734889
    [35]
    ZHANG T W, ZHANG X L, KE X, et al. HOG-ShipCLSNet: a novel deep learning network with hog feature fusion for SAR ship classification[J]. IEEE T Geosci Remote, 2021, 60: 1-22.
    [36]
    YU Q, YANG C Z, FAN H H, et al. Latent-MVCNN: 3D shape recognition using multiple views from pre-defined or random viewpoints[J]. Neural Process Lett, 2020, 52(1): 581-602. doi: 10.1007/s11063-020-10268-x
    [37]
    ALAM M T, KUMAR V, KUMAR A. A multi-view convolutional neural network approach for image data classification[C]//International Conference on Communication information and Computing Technology (ICCICT), Online, IEEE, 2021: 1-6.
    [38]
    YANG J, YANG G C. Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer[J]. Algor, 2018, 11(3): 1-15.
    [39]
    POOJARY R, PAI A. Comparative study of model optimization techniques in fine-tuned CNN models[C]//International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, United Arab Emirates. IEEE, 2019: 1-4.
    [40]
    LYDIA A, FRANCIS S. AdaGrad: an optimizer for stochastic gradient descent[J]. Int J Inf Comput Sci, 2019, 6(5): 566-568.
    [41]
    EOM S H. Developing sentimental analysis system based on various optimizer[J]. Internat J Intt, Broa Commun, 2021, 13(1): 100-106.
    [42]
    RABBY S F, HASAN A, SOEB M J A, et al. Performance analysis of different convolutional neural network (CNN) models with optimizers in detecting tuberculosis (TB) from various chest X-ray images[J]. Euro J Enginee Tec Res, 2022, 7(4): 21-30. doi: 10.24018/ejeng.2022.7.4.2861
    [43]
    SHI H Z, YANG N S, TANG H, et al. ASGD: sochastic gradient descent with adaptive batch size for every parameter[J]. Math, 2022, 10(6): 1-15.
    [44]
    PAYMODE A S, MALODE V B. Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG[J]. Art Intell Agric, 2022, 6: 23-33.
    [45]
    KHAN S, RAHMANI H, SHAH S A, et al. A guide to convolutional neural networks for computer vision[J]. Synth Lect Comput Vis, 2018, 8(1): 1-207.
    [46]
    SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. IEEE, 2016: 2818-2826.
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