Bulldogs Nose Detection using Deep Learning

Joyce Katiuccia Medeiros Ramos Carvalho; Pedro Henrique Neves da Silva; Sandra Adriana Uhry; Gustavo da Silva Andrade; Gisele Braziliano de Andrade; Hemerson Pistori; Newton Loebens

doi:10.22456/2175-2745.143541

Authors

Joyce Katiuccia Medeiros Ramos Carvalho Universidade Católica Dom Bosco (UCDB)
Pedro Henrique Neves da Silva Universidade Federal de Mato Grosso do Sul (UFMS) https://orcid.org/0000-0002-6983-3049
Sandra Adriana Uhry Universidade Católica Dom Bosco (UCDB)
Gustavo da Silva Andrade Universidade Federal de Mato Grosso do Sul (UFMS) https://orcid.org/0009-0004-0248-7083
Gisele Braziliano de Andrade Universidade Católica Dom Bosco (UCDB)
Hemerson Pistori Universidade Católica Dom Bosco (UCDB) https://orcid.org/0000-0001-8181-760X
Newton Loebens Universidade Federal do Pampa (UNIPAMPA) https://orcid.org/0000-0001-6309-9309

DOI:

https://doi.org/10.22456/2175-2745.143541

Keywords:

Computer Vision, Neural Networks, Brachycephalic Obstructive Airway Syndrome, Stenotic Nares, Bulldogs Nose

Abstract

An animal nose identification system could allow efficient monitoring of pets, and it can be used in applications such as identifying animal breeds or possible diseases and/or injuries. For that, a bulldog image dataset was build from French bulldogs. To carry out the validation through cross-validation, 10 folds (K-folds) were created. Afterward, five convolutional neural networks (CNN) were trained with our dataset to identify the nose: Faster R-CNN (Region-based CNN), SABL (Side-Aware Boundary Localization), RetinaNet (ResNet50+FPN), VFNet (VarifocalNet), and ATSS (Adaptive Training Sample Selection). Faster R-CNN, SABL, RetinaNet, VFNet and ATSS were used for training in the first phase, while ATSS, Faster R-CNN, ATSS and SABL in the second. The results showed that the ATSS network obtained the highest values of mAP and Accuracy in the first phase. Moreover, SABL network achieved the highest values
of mAP50, mAP75, Recall, F-Score and Accuracy at the end of the second phase.

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