关键词: 羊肉架切割, DeepLabv3+, 图像分割, 特征提取, XGBoost

Abstract:

To realize the automatic cutting of sheep’s trunk, the cutting methods of sheep’s trunk based on DeepLabv3+ and XGBoost is proposed. By studying DeepLabv3+ network architecture, four basic networks are designed based on ResNet-101. The ASPP structure is improved by modifying expansion rate and introducing deformable convolution. Eight kinds sheep’s trunk segmentation networks are built, and the data sets are divided according to the ratio of 6∶2∶2, to compare with DeepLabv3+. The mIoU, PA and F value of the optimized DeepLabv3+ are 0.849, 0.870, 0.879, which can better meet the requirements of the segmentation task of the characteristic parts of the sheep’s trunk. Based on the image segmentation, the features of the skeleton are extracted. A total of 35 configuration bit feature parameters are obtained, and the feature set is normalized and processed. Prediction model is established based on XGBoost, the MSE and R2 are 8.18 and 0.949, the mean absolute value of coordinate residual is 2.47. It shows that the model has strong prediction ability and generalization ability. The experiment is carried out on robot platform. The error is about 3.25 mm and the theoretical efficiency has reached 413 units per hour, which increased by 37.9%. The results show that the method is feasible and has high precision.

Key words: the cutting of sheep’s trunk, DeepLabv3+, image segmentation, feature extraction, XGBoost