| Revista: | Latin-American Journal of Computing (LAJC) |
| Base de datos: | |
| Número de sistema: | 000565116 |
| ISSN: | 1390-9134 |
| Autores: | Silva, Carlos1 Welfer, Daniel2 |
| Instituciones: | 1Federal Institute Farroupilha, 2Universidade Federal de Santa Maria, |
| Año: | 2019 |
| Volumen: | 6 |
| Número: | 1 |
| Paginación: | 9-16 |
| País: | Ecuador |
| Idioma: | Inglés |
| Resumen en inglés | A tool that can perform the automatic identification of cattle brandings is essential for the government agencies responsible for the record, control and inspection of this activity. This article presents a novel hybrid method that uses Convolutional Neural Networks (CNN) to extract features from images and Support Vector Machines (SVM) to classify the brandings. The experiments were performed using a cattle branding image set provided by the City Hall of Bagé, Brazil. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93.11% in the first experiment with 39 brandings and 1,950 sample images, and 95.34% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 31.661s and 41.749s, respectively. |
| Keyword: | Cattle Branding, Support Vector Machines, Convolutional Neural Network |
| Texto completo: | Texto completo (Ver PDF) |
