Deep Convolutional Neural Networks based on VGG-16 Transfer Learning for Abnormalities Peeled Shrimp Classification

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Tamnuwat Valeeprakhon
Korawit Orkphol
Penpun Chaihuadjaroen


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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Valeeprakhon, T., Orkphol, K., & Chaihuadjaroen, P. . (2022). Deep Convolutional Neural Networks based on VGG-16 Transfer Learning for Abnormalities Peeled Shrimp Classification. INTERNATIONAL SCIENTIFIC JOURNAL OF ENGINEERING AND TECHNOLOGY (ISJET), 6(2), 13–23. Retrieved from
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