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Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network by Huang, Endai; Mao, Axiu; Hou, Junhui; Wu, Yongjian; Xu, Weitao; Ceballos, Maria Camila; Parsons, Thomas D.; Liu, Kai is a scholarly article available to read on EtoBox.

What is Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network about?

Computer vision enables the development of new approaches to monitor the behavior, health, and welfare of animals. Instance segmentation is a high-precision method in computer vision for detecting individual animals of interest. This method can be used for in-depth analysis of animals, such as examining their subtle interactive behaviors, from videos and images. However, existing deep-learning-based instance segmentation methods have been mostly developed based on public datasets, which largely omit heavy occlusion problems; therefore, these methods have limitations in real-world applications involving object occlusions, such as farrowing pen systems used on pig farms in which the farrowing crates often impede the sow and piglets. In this paper, we adapt a Center Clustering Network originally designed for counting to achieve instance segmentation, dubbed as CClusnet-Inseg. Specifically, CClusnet-Inseg uses each pixel to predict object centers and trace these centers to form masks based on clustering results, which consists of a network for segmentation and center offset vector map, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Centers-to-Mask (C2M)

Author

Huang, Endai; Mao, Axiu; Hou, Junhui; Wu, Yongjian; Xu, Weitao; Ceballos, Maria Camila; Parsons, Thomas D.; Liu, Kai

Published

2022

Language

EN