Year: 2024
Type: conference-paper
Abstract: We introduced IYOLO-FAM (Improved YOLOv8 with Feature Attention Mechanism) for detecting cow behaviours. By leveraging the robust YOLOv8 architecture improved with Feature Attention Mechanisms (FAM), Squeeze-and-Excitation (SE) blocks and data augmentation techniques, we enhanced the ability of the model to focus on salient features and generalize across a diverse farm environment. The experimental results demonstrated that IYOLO-FAM outperforms baseline YOLO models, achieving a mean Average Precision ($\mathbf{m A P}$) of $88 \%$ at an IoU threshold of 0.5 and $70 \%$ across IoU thresholds from 0.5 to 0.95. These results highlighted substantial improvements over previous versions, particularly in detecting specific cow behaviours such as eating, lying, standing, and walking. The integration of SE blocks and FAM within the YOLOv8 framework proved effective in highlighting relevant features and enhancing detection accuracy, underscoring the significance of integrating advanced deep learning techniques with robust data augmentation techniques to tackle the challenges posed by a real-world farm environment. The proposed approach has the potential to benefit animal welfare in real-world applications, with future research focusing on integrating multimodal data. Additionally, real-world trials will validate the model’s robustness and effectiveness in a practical farm environment.
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
Awards: G1281209674, G6053242198, G8729322013
OpenAlex ID: W4404564143