Objective Swine limb and hoof disease is one of the significant reasons for culling breeding swine, resulting in substantial economic losses for livestock farms. The diagnosis of swine limb and hoof disease typically relies on manual observation of pig gaits, which consumes high labor costs and has low efficiency. The aim of this study is to achieve automated pig gait scoring, and efficiently determine the health status of swine limb and hoof.

Method This study proposed an “end-to-end” pig gait scoring method. Videos of individual breeding swine passing through designated channels were collected and a four-point gait dataset was created. Deep learning techniques were employed for video analysis. A time attention module (TAM) based on a 3D convolutional neural network was designed to effectively extract feature information between video frame images. By combining TAM with residual structures, the pig gait scoring model TA3D was constructed for feature extraction and gait classification scoring in the gait videos. To further improve model performance and achieve automation, the gait focus module (GFM) was designed. GFM could autonomously extract effective information from real-time video streams to synthesize high-quality gait videos, improving model performance while reducing computational costs.

Result The experimental results demonstrated that GFM could operate in real-time and reduced the size of gait videos by over 90%, significantly reducing storage cost, and the gait scoring accuracy of the TA3D model was 96.43%. Moreover, the comparison test results with other classic video analysis models showed that TA3D achieved optimal levels of accuracy and inference speed.

Conclusion This paper proposes a solution that can be applied to the automatic scoring of pig gait, providing a reference for the automatic detection of swine limb and hoof disease.