Wild boar recognition using convolutional neural networks

• Published in: Concurrency and computation Vol. 33; no. 22
• Main Authors: Silva, Luís Carlos, Pádua, Maricéia B. S., Ogusuku, Leonardo M., Keese Albertini, Marcelo, Pimentel, Renato, Backes, André R.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 25.11.2021; Wiley Subscription Services, Inc
• Subjects: Artificial neural networks; Control methods; convolutional neural networks; image classification; Native species; Neural networks; River banks; Species classification; Water flow; wildlife monitoring
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6010

Summary Wild boar (Sus scrofa) is a destructive species of swine. They spread diseases, represent a threat to native species, and destroy natural habitats by destabilizing river banks, thus reducing water flow. The monitoring of populations of wild boars is central to the execution and evaluation of methods to control them. To address this issue, in this article, we retrain and apply four convolutional neural networks (CNNs; AlexNet, VGG‐16, Inception‐v3, and ResNet‐50) to classify different species of “bush pigs” in real‐world footage: two native species of the Brazilian fauna, collared peccary (Pecari tajacu) and white‐lipped peccary (Tayassu pecari), and one invasive species, wild boar (S. scrofa). Results show that CNN can be used to classify animals with very similar behavior and appearance and that ResNet‐50 outperforms all compared CNN in terms of accuracy (98.33%) and the lowest probability of false positives (i.e., native species classified as wild boar).