MaskDis R-CNN: An instance segmentation algorithm with adversarial network for herd pigs

Abstract

The current instance segmentation method can achieve satisfactory results in common scenarios. However, under the overlap or partial occlusion between targets caused by the complex scenes, accurate segmentation of pigs remains a challenging task. To address the problem, the authors propose an instance segmentation method based on Mask Scoring region-based convolutional neural networks (R-CNN) (MS R-CNN), which creates the adversarial network called MaskDis in the head branch of MS R-CNN. The MaskDis is trained as a discriminator using a generative adversarial network, and the MS R-CNN model is used as a generator during model training. The adversarial training enables the generator to learn context information and features at the pixel level, which effectively improves the segmentation quality under pigs’ overlapping or dense occlusions scenes. Experimental conducted on the pig object segmentation dataset show that the proposed approach achieves a precision of 92.03%, a recall of 92.18%, and an F1 score of 0.9210. Compared with the basic MS R-CNN model, the approach achieved a 2.25% improvement in precision and 1.18% improvement in F1 score. Furthermore, the improved approach outperformed advanced instance segmentation methods such as YOLACT, Swin Transformer, YOLOv5-seg, and SOLOv2 on COCO evaluation metrics. These experimental results demonstrate the effectiveness of the proposed approach in instance segmentation of pigs in complex scenes, providing technical support for non-contact pig automatic management.