Estimation of the pig’s limb orientation and gripping points based on the pose estimation deep neural networks

Abstract

The robotization of the pig carcass slaughtering process requires the possibility of automatic identification of the pig’s body position and orientation of its individual parts for further gripping and manipulation of the limbs. This paper presents a method for locating the gripping points on the pig limbs based on pose estimation of a pig carcass fixed in a meat factory cell from RGB-D images of carcasses taken from 6 different views. A deep learning model based on U-Net architecture was proposed to solve the problem of keypoint detection to estimate the pose and gripping points of pig carcasses. The proposed method demonstrates high precision and robustness in estimating the gripping points of pig limbs: Norwegian style gripping points - mAP(0.5…0.95) = 0.9504, mAR(0.5…0.95) = 0.9688, distance error is within 15 mm; Danish style gripping points - mAP(0.5…0.95) = 0.9831, mAR(0.5…0.95) = 0.9937, distance error is within 15 mm.