Thai Venomous Snake Identification using Yolo
Conference proceedings article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Areerut Wongmaha and Tuul Triyason
Publication year: 2024
Start page: 1
End page: 6
Number of pages: 6
URL: https://wi-iat2024.sit.kmutt.ac.th/register/program-event-2024.html
Languages: English-United States (EN-US)
Abstract
Thailand's diverse ecosystem harbors numerous snake species, often causing venomous snake bites necessitating antivenom treatment. Accurate identification of snake species enhances treatment success. This study classified 12 Venomous Snake with medical importance in Thailand, using 6,555 annotated images from iNaturalist, Nick Wildlife and the Queen Saovabha Memorial Institute. Images were annotated and split into training, testing and validation sets. YOLOv7, YOLOv8 and YOLOv9 models were employed with batch size 16, image size 640, and epochs 50, 100 and 150. YOLOv9m excelled with Precision 0.85, Recall 0.815, outperforming YOLOv9s and YOLOv9c. The results show that, YOLOv9m proved the most effective for accurate detection, especially in complex environments for venomous snake species differentiation.
Keywords
Computer Vision, image classification, snake identification, YOLO