A Method for Pig Behavior Target Detection Based on the Lightweight RTMDet Model

With the rapid development of modern computer vision technology, machine vision has demonstrated tremendous potential in the field of smart breeding. Through machine vision, the health status of livestock during the breeding process can be scientifically monitored, especially in pig farming, where the health condition of pigs can be assessed by collecting their behavior. This paper details the reconstruction of the Edinburgh pig behavior dataset and provides an overview of the corresponding annotation methods. It also describes the improvements made to the RTMdet model, including the replacement of the backbone network with MobileNetV3 and modifications to the neck module. These enhancements result in a model with slightly lower accuracy compared to the original but significantly faster processing speed. Additionally, the paper discusses the deployment of the model on the Jetson Nano computing platform for edge device-based detection.