TY  - CONF
AU  - Patricia Suarez
AU  - Angel Sappa
AU  - Dario Carpio
AU  - Henry Velesaca
AU  - Francisca Burgos
AU  - Patricia Urdiales
A2  - ISVC
PY  - 2022//
TI  - Deep Learning Based Shrimp Classification
BT  - 17th International Symposium on Visual Computing
SP  - 36–45
VL  - 13598
KW  - Pigmentation
KW  - Color space
KW  - Light weight network
N2  - 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.
UR  - https://link.springer.com/chapter/10.1007/978-3-031-20713-6_3
L1  - http://refbase.cvc.uab.es/files/SAC2022.pdf
UR  - http://dx.doi.org/10.1007/978-3-031-20713-6_3
N1  - MSIAU; no proj
ID  - Patricia Suarez2022
ER  -