부산대학교 도서관

Simple Summary: The abnormal behavior of pigs can undermine their growth performance and economic value. Therefore, the precision and timeliness of behavioral recognition are crucial for maintaining pig health and advancing intelligent farming. This study introduces an innovative DM-GD-YOLO model, an optimized variant of YOLOv8, which integrates a deformable convolution module with enhanced multi-path coordinate attention and a gather-and-distribution mechanism. Through experiments conducted on a farm with about 30 pigs per pen, the results reveal that the proposed model can effectively recognize four common behaviors (walking, lying, sniffing, and kneeling) and three abnormal behaviors (fighting, mounting, and fence climbing) in pigs. Compared to traditional methods, the model exhibits superior performance and provides a practical solution for enhancing the welfare of pigs. The behavior of pigs is intricately tied to their health status, highlighting the critical importance of accurately recognizing pig behavior, particularly abnormal behavior, for effective health monitoring and management. This study addresses the challenge of accommodating frequent non-rigid deformations in pig behavior using deformable convolutional networks (DCN) to extract more comprehensive features by incorporating offsets during training. To overcome the inherent limitations of traditional DCN offset weight calculations, the study introduces the multi-path coordinate attention (MPCA) mechanism to enhance the optimization of the DCN offset weight calculation within the designed DCN-MPCA module, further integrated into the cross-scale cross-feature (C2f) module of the backbone network. This optimized C2f-DM module significantly enhances feature extraction capabilities. Additionally, a gather-and-distribute (GD) mechanism is employed in the neck to improve non-adjacent layer feature fusion in the YOLOv8 network. Consequently, the novel DM-GD-YOLO model proposed in this study is evaluated on a self-built dataset comprising 11,999 images obtained from an online monitoring platform focusing on pigs aged between 70 and 150 days. The results show that DM-GD-YOLO can simultaneously recognize four common behaviors and three abnormal behaviors, achieving a precision of 88.2%, recall of 92.2%, and mean average precision (mAP) of 95.3% with 6.0MB Parameters and 10.0G FLOPs. Overall, the model outperforms popular models such as Faster R-CNN, EfficientDet, YOLOv7, and YOLOv8 in monitoring pens with about 30 pigs, providing technical support for the intelligent management and welfare-focused breeding of pigs while advancing the transformation and modernization of the pig industry. [ABSTRACT FROM AUTHOR]