Lightweight Domestic Pig Behavior Detection Based on YOLOv8

The prevalence and magnitude of extensive domestic pig breeding in China are rising, and behavioral assessment of these pigs is essential for enhancing production efficiency. The existing behavior identification technique for domestic pigs is computationally intensive, making it challenging to use on edge devices. This study introduces a lightweight method for identifying domestic pig behavior, YOLOv8-PigLite, derived from YOLOv8. Initially, a novel two-branch bottleneck module is developed within the C2f module, incorporating average pooling and deep convolution (DWConv) in one branch, while the other branch utilizes maximum pooling and DWConv to augment multi-scale feature representation. Subsequently, a Grouped Convolution module is integrated into the convolution framework, followed by incorporating the SE module to diminish the recognition error rate further. Ultimately, we implement BiFPN in the neck network to replace the original FPN, which streamlines the neck network and enhances its feature-processing capabilities. The test findings indicated that, in comparison to the original YOLOv8n model, the precision, recall, and mean average precision at 50% remain constant, while the parameters and floating-point computations are diminished by 59.80% and 39.50%, respectively. Additionally, the FPS has increased by 32.61%, and the model’s generalizability has been validated on public datasets.