Extraction of pig contour based on fully convolutional networks

Objective  To realize non-contacting and low-cost pig contour extraction under the piggery scene.

Method  We took individual pig in the real culture environment as the research object, and proposed a full convolutional neural network model based on the combination of VGG16 and UNET (VGG-UNET model). We adopted the batch processing method in this model to transfer and learn the parameters of VGG16 model. We achieved semantic level segmentation of the image by combining the deep abstract feature and shallow feature in depth via building the duplicate channel. The model was verified on 1 815 datasets of 30 Large White× Landrace pigs. Comparison experiments of different batch sizes were performed, and three groups with the best results were selected to explore the relationship between batch size and the evaluation index.

Result  Through comparison experiments on datasets, the pixel accuracy and mean intersection-over-union of VGG-UNET model were 94.32% and 86.60% respectively, which were 0.89% and 1.67% higher than those of the UNET model. The experiments showed different relationship between the change of different index values and batch size. Batch size had no obvious impact on the convergence rate of the model under this experimental environment. Through comprehensive analysis of PA and MIoU index values under different batch sizes, the VGG-UNET model showed the highest stability and robustness, and it was found to be the best when the batch size was 8.

Conclusion  The VGG-UNET model is effective for accurate and stable extraction of pig contour. Such segmentation result is relatively complete and the model has higher robustness, which can provide a reference for follow-up identification of individual pigs.