Deep Convolutional Neural Networks based on VGG-16 Transfer Learning for Abnormalities Peeled Shrimp Classification
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Abstract
The peeled white leg shrimp is an important exports economic product of many countries with in the coastal areas. In the shrimp processing, shrimps were beheaded and peeled with an automatic machine; after that, the peeled shrimps will be inspected to classify the good peeled shrimps out from the abnormal peeled shrimps. However, the inspection process was conducted in manual work, and this may result in misinterpretation and reduce the manufacturing efficiency due to exhausted humans. Nowadays, machine learning play an important role in food industries for monitoring the manufacturing quality activities. This powerful technology have made significant breakthroughs in this filed and their performances greatly surpass the human work. In this paper, deep convolutional neural network based on VGG-16 transfer learning for abnormalities peeled shrimp classification was proposed. This method makes up with two main steps are data preprocessing and model network definition. The dataset preprocessing aims to prepare the provided dataset to suit our classification model by aligning images into the C shape pattern, cropping, and resizing images to meet the need of VGG-16 input layer requirement. The network model definition aims to build the classification model by considering the VGG-16 transfer learning model. This model was customized by replacing the old classification layer with our designed fully connected layers and was trained by using the best parameters conducted in the experimental process. The results obtained from our VGG-16 model achieves 98.36% of accuracy with 0.0213 seconds for classification time, and it produces better results than other comparative models.
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M. Z. Herzberg, A. I. C. Córdova, and R. O. Cavallic,“Biological Viability of Producing White Shrimp Litopenaeus Vannamei in Seawater Floating Cages,” Aquaculture, vol. 259, no. 1-4, pp. 283-289, Sep. 2006.
A. Farhana, “The Export Trend of Shrimp Industries in Bangladesh: An Analysis,” Scientifc Research Journal, vol. 5, no. 4, pp. 80-95, Sep. 2017.
R. Warangkhana, “Model for Forecasting the Export Volume of Frozen Shrimp,” RMUTSB Academic Journal, vol. 8, no. 1, pp. 70-82, Jun. 2020.
L. M. Díaz-Tenorio, F. García-Carreño, and Ramon PachecoAguilar “Comparison of Freezing and Thawing Treatments On Muscle Properties of White Leg Shrimp (Litopenaeus Vannamei),” Journal of Food Biochemistry, vol. 31, no. 5, pp. 563-576, Oct. 2007.
C. Jotikasthira, “The Development of Thailand’s Potentials and Exports of Shrimp Processed for Southeast Asia,” Journal of Bangkokthonburi University, vol. 8, no. 1, pp. 59-72, Jan. 2019.
D. Sylvain, “Shrimp Quality and Safety Management Along the Supply Chain in Benin,” Ph.D. thesis, Dept. Nutrition and Health Sciences, Wageningen Univ. Wageningen, NLD, 2015.
G. A. Leiva-Valenzuela and J. M. Aguilera, “Automatic Detection of Orientation and Diseases in Blueberries Usingimage Analysis to Improve Their Postharvest Storage Quality,” Journal of Food Control, vol. 33, pp. 166-173, Sep. 2013.
H. Zareiforoush, S. Minaei, M. R. Alizadeh et al., “Design, Development And Performance Evaluation of an Automatic Control System for Rice Whitening Machine Based on Computer Vision and Fuzzy Logic,” Computers and Electronics in Agriculture, vol. 124, pp. 14-22, Jun. 2016.
P. A. Coelho, S. N. Torres, W. E. Ramírez et al., “A machine Vision System For Automatic Detection of Parasites Edotea Magellanicain Shell-off Cooked Clammulinia Edulis,” Journal of Food Engineering, vol. 181, pp. 84-91, Jul. 2016.
T. T. N. Thai, N. S. Thanh, and P. C. Viet, “Computer Vision Based Estimation of Shrimp Population Density and Size,” in Proc. International Symposium on Electrical and Electronoces (ISEE), 2021. pp.145-148.
R. P. S. Sankar, V Jenitta, and B Kannapiran, “Diagnosis of Shrimp Condition Using Intelligent Technique,” in Proc. International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), 2017, pp.1-5.
L. J. Dah, X. Guangming, R. M. Lane et al., “An Efcient Shape Analysis Method for Shrimp Quality Evaluation,” in Proc. International Conference on Control, Automation, Robotics and Vision (ICARCV), 2012, pp. 865-870.
D. Zhang, K. D. Lillywhite, D. J. Lee et al., “Automatic Shrimp Shape Grading Using Evolution Constructed Features,” Computers and Electronics in Agriculture, vol. 100, pp. 116-122, Jan. 2014.
Z. Liu, C. Fang, and Z. Wei, “Recognition-Based Image Segmentation of Touching Pairs of Cooked Shrimp (Penaeus Orientalis) Using Improved Pruning Algorithm for Quality Measurement,” Journal of Food Engineering, vol. 195, pp.166-181, Feb. 2017.
K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A Survey of Transfer Learning,” Journal of Big Data, pp. 1-40, May. 2016.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Computer Vision and Pattern Recognition, vol. 6, pp. 1-14, Sep. 2015.
G. Q. Jiang, C.-J. Zhao, and J.-Y. Qi, “The Research of Image Segmentation Based on Color Characteristic,” in Proc. International Conference on Machine Learning and Cybernetics, 2011. pp 1-12,
S. Bansal and D. Aggarwal, “Color Image Segmentation Using Cielab Color Space Using ant Colony Optimization,” International Journal of Computer Applications, vol. 29, no. 9, pp. 28-34, Sep. 2011.
P. Ganesan, V. Rajini, B. S. Sathish et al., “HSV Color Space Based Segmentation of Region of Interest in Satellite Images,” in Proc. International Conference on Control, Instrumentation, Communication and Computational Technologies, 2014, pp. 1-10.
C. Zhang, W. Yang, Z. Liu et al., “Color image segmentation in rgb color space based on color saliency,” in Proc. International Conference on Computer and Computing Technologies in Agriculture, 2013, pp. 101-105.
K. B. Shaik, P. Ganesan, and V. Kalist, “Comparative Study of Skin Color Detection And Segmentation in HSV and YCbCr Color Space,” Procedia Computer Science, vol. 57, pp. 41-48, Dec. 2015.
N. Jamil, T. M. T. Sembok, and Z. A. Bakar, “Noise Removal and Enhancement of Binary Images Using Morphological Operations,” in Proc. International Symposium on Information Technology, 2008, pp. 1-6.
P. Maragos and R. W. Schafer, “Morphological Skeleton Representation and Coding of Binary Images,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 5, pp. 1228-1244, Oct. 1986.
G. Toussaint, “Solving Geometric Problems with the Rotating Calipers,” in Proc. IEEE MELECON 83, 2000, pp. 1-12.
S. Suzuki and K. Abe, “Topological structural analysis of digitized binary images by border following,” Computer Vision, Graphics, and Image Processing, vol. 30, no. 1, pp. 32-46, Apr. 1985.
J. Gu, P. Yu, X. Lu et al., “Leaf Species Recognition Based on VGG16 Networks and Transfer Learning,” in Proc. IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference, 2021, pp. 2189-2193.
M. Sokolova, N. Japkowicz, and S. Szpakowicz, “Beyond Accuracy, F-Score and Roc: A Family of Discriminant Measures For Performance Evaluation,” Advances in Artifcial Intelligence, vol. 4304, pp. 1015-1021, Jan. 2006.
T. Viering and M. Loog, “The Shape of Learning Curves: A Review,” ArXiv, vol. 1, pp. 1-46, Mar. 2021.
A. Saad, A. M. Tareq, and A.-Z. Saad, “Understanding of a Convolutional Neural Network,” in Proc. International Conference on Engineering and Technology, 2017, pp. 1-6.
B. Siddhartha, S. Václav, and K. Ashish. Recurrent Neural Network. Elsevier Academic Press, Massachusetts: USA, 2020, pp. 52-64.
Z. Yongli, “Support Vector Machine Classifcation Algorithm and its Application,” in Proc. International Conference on Information Computing and Applications, 2012, pp.179-186.
A. G. Howard, M. Zhu, B. Chen et al., “Mobilenets: Efcient Convolutional Neural Networks for Mobile Vision Applications,” Computer Vision and Pattern Recognition, vol. 1, pp. 1-9, Apr. 2017.
F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1-8.
K. He, X. Zhang, S. Ren et al., “Deep residual Learning for Image Recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2016 pp. 770-778.
G. Huang, Z. Liu, L. V. D. Maaten et al., “Densely Connected Convolutional Networks,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1-9.
C. Szegedy, V. Vanhoucke, S. Ioffe et al., “Rethinking the Inception Architecture for Computer Vision,” in Proc. IEEE Conference Computer Vision and Pattern Recognition, 2015, pp. 4700-4708.