JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
Airborne Species Detection using YOLOv8.
Pooja L, Mr. Sampath kumar S
Nowadays, the majority of avian species are uncommon to find, and when they are, it might be challenging to classify them. In the natural world, birds come in a variety of sizes, shapes, and colors as viewed. Because of the variability in bird appearance, new procedures are necessary for reliable detection and conservation, which effects identification and monitoring operations. to provide a reliable method that facilitates accurate and quick identification of bird species, supporting successful conservation initiatives. This paper uses the YOLOv8 object identification algorithm to do a thorough analysis on avian species recognition. YOLOv8, a sophisticated object identification method based on convolutional neural networks (CNNs), is well-known for its quick and accurate inference processes, making it appropriate for real-time detection and classification applications. The initiative intends to greatly improve avian monitoring efforts by utilizing the YOLOv8 algorithm, supporting environmental study and biodiversity protection.
Deep learning, avian species detection and YOLOv8.