PLM-YOLOv5: Improved YOLOv5 for Pig Detection in Livestock Monitoring

With the rapid development of smart agriculture, the demand for pig monitoring has been increasing, particularly in disease prevention and behavioral analysis. Real-time monitoring of pig behavior is essential for improving farming efficiency and ensuring animal welfare. However, challenges such as pigs’ varying body sizes, movement, and complex environments make accurate detection and tracking difficult. To address these issues, this paper proposes an improved YOLOv5 model, named PLM-YOLOv5, specifically designed for pig detection in livestock monitoring. First, we add an additional detection head to enhance the model’s capability to detect smaller pigs effectively. Next, we integrate the Convolutional Block Attention Module (CBAM) to enhance feature extraction, particularly in handling complex backgrounds. Finally, we utilize a self-collected dataset of over 1,400 images for model training and validation, ensuring the model adapts to real-world livestock environments. Experimental results show that PLM-YOLOv5 achieves significant improvements in pig detection tasks, with the mean Average Precision (mAP) increasing by approximately 9% compared to the original YOLOv5. Additionally, training and validation loss curves demonstrate the model's stability and convergence. These results confirm the effectiveness and reliability of PLM-YOLOv5 for real-world applications, offering a robust solution for smart agriculture.