BTENet: Back-Fat Thickness Estimation Network for Automated Grading of the Korean Commercial Pig

Author:

Lee Hyo-Jun,Baek Jong-Hyeon,Kim Young-KukORCID,Lee Jun HeonORCID,Lee Myungjae,Park Wooju,Lee Seung Hwan,Koh Yeong JunORCID

Abstract

For the automated grading of the Korean commercial pig, we propose deep neural networks called the back-fat thickness estimation network (BTENet). The proposed BTENet contains segmentation and thickness estimation modules to simultaneously perform a back-fat area segmentation and a thickness estimation. The segmentation module estimates a back-fat area mask from an input image. Through both the input image and estimated back-fat mask, the thickness estimation module predicts a real back-fat thickness in millimeters by effectively analyzing the back-fat area. To train BTENet, we also build a large-scale pig image dataset called PigBT. Experimental results validate that the proposed BTENet achieves the reliable thickness estimation (Pearson’s correlation coefficient: 0.915; mean absolute error: 1.275 mm; mean absolute percentage error: 6.4%). Therefore, we expect that BTENet will accelerate a new phase for the automated grading system of the Korean commercial pig.

Funder

Chungnam National University

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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