Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming

Abstract

In precision livestock farming, computer vision based approaches have been widely used to obtain individual cattle health and welfare information such as body condition score, live weight, activity behaviours. For this, precisely segmenting each cattle image from its background is a prerequisite, which is an important step towards obtaining real-time individual cattle information. In this paper, an instance segmentation approach based on a Mask R-CNN deep learning framework is proposed to solve cattle instance segmentation and contour extraction problems in a real feedlot environment. The proposed approach consists of the following steps: key frame extraction (detect the huge cattle motion frames), image enhancement (reduce the illumination and shadow influence), cattle segmentation and body contour extraction. We trained and tested the proposed approach on a challenging cattle image dataset. According to the experimental results, the proposed approach can render fairly desirable cattle segmentation performance with 0.92 Mean Pixel Accuracy (MPA) and achieve contour extraction with an Average Distance Error (ADE) of 33.56 pixel, which is better than that of the state-of-the-art SharpMask and DeepMask instance segmentation methods.