Health evaluation and dangerous reptile detection using a novel framework powered by the YOLO algorithm to design high‐content cellular imaging systems

Abstract

Abstract This novel approach in animal biology could revolutionize identifying endangered species, addressing the issue of misclassifying potentially harmful animals based solely on visual characteristics. Particularly impactful for farmers in agricultural fields, it aims to reduce the heightened risk of venomous animal attacks, ultimately improving safety. Due to a lack of accessible education, illiterate farmers are more susceptible to adopting superstitious beliefs, which tragically leads to fatal snakebites even when medical treatment is readily available. Furthermore, environmental factors can unexpectedly hold typically non‐threatening animals responsible for a large number of human deaths each year. However, the complexity of human recognition of these hazards has prompted the development of a novel design approach aimed at simplifying the process. Integration of the ResNet learning algorithm in conjunction with You Only Look Once (YOLOv5) within the framework is recommended to facilitate real‐time processing and improve accuracy. This combined approach not only speeds up animal recognition but also takes advantage of ResNet's deep learning capabilities. The first phase entails deploying YOLOv5 to detect the presence of snakes in the proposed study, achieving a remarkable 87% precision in snake detection thanks to the synergistic fusion of ResNet and YOLOv5.

Keywords

• computer vision
• computer vision and image processing