Open AccessComputational classification of animals for a highway detection system
Author(s) -
Denis Sato,
Adroaldo José Zanella,
Ernane José Xavier Costa
Publication year - 2021
Publication title -
brazilian journal of veterinary research and animal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.187
H-Index - 17
eISSN - 1678-4456
pISSN - 1413-9596
DOI - 10.11606/issn.1678-4456.bjvras.2021.174951
Subject(s) - convolutional neural network , computer science , artificial intelligence , machine learning , pattern recognition (psychology)
Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.