Hatching Egg Image Segmentation Based on Dense Blocks and the Hierarchical Sampling of Pixels

Abstract

Fertility detection of hatching eggs is crucial in the manufacturing of vaccines. For hatching egg images, the segmentation results of blood vessels, cracks and air chambers are important for detecting the fertility of hatching eggs. In this paper, we propose an image segmentation method based on dense blocks and the hierarchical sampling of pixels. Dense blocks are used instead of the traditional layer-by-layer structure to improve efficiency and model robustness. The hierarchical sampling of pixels uses small batch sampling to add diversity during batch updates, which can accelerate learning. The sampled features are sparsely arranged and classified using an Multi-layer Perceptron(MLP), which can introduce complex nonlinear predictors and improve accuracy. The experimental results show that the MIoU reaches 90.5%. The proposed method can significantly improve the segmentation performance