Year: 2025
Type: article
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.
Authors: 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
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407772910