Table 5. Comparison of improved mask R-CNN and other models for pig posture detection to provide insights into the relative accuracy and efficiency of each approach.

Model	AP				mAP	References
Model	Standing	Sitting	Lying	Eating	mAP	References
YOLOv5s	99.4	98.7	98.0		86.8	[59]
YOLOv5 + EfficientNet	0.67		0.81		0.899	[60]
Yolov3	0.97	0.96	0.88		0.918	[61]
Faster R–CNN + NASNet	0.81		0.78		0.802	[45]
Faster R–CNN	0.90		0.84		0.891	[62]
Faster R–CNN + ResNet101	0.87		0.86		0.856	[44]
R-FCN + ResNet101	0.88		0.88		0.881
SSD + Inception V2	0.69		0.70		0.693
R–FCN + ResNet101	0.95	0.90	0.73		0.872	[42]
Faster R–CNN–Resnet 50	0.86	0.91	0.84		0.845	[63]
Mask R–CNN–ResNeXt 101 (piglet)	0.97	0.92	0.89	0.95	0.937	This study
Mask R–CNN-ResNeXt 101 (pig)	0.97	0.91	0.88	0.96	0.935	This study

YOLO, you only look once; R–CNN, regions with convolutional neural networks; FCN, fully convolutional network.