PT Unknown
AU Patricia Suarez
   Angel Sappa
   Dario Carpio
   Henry Velesaca
   Francisca Burgos
   Patricia Urdiales
TI Deep Learning Based Shrimp Classification
BT 17th International Symposium on Visual Computing
PY 2022
BP 36–45
VL 13598
DI 10.1007/978-3-031-20713-6_3
DE Pigmentation; Color space; Light weight network
AB This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model.
ER