Cattle Segmentation and Contour Detection Based on Solo for Precision Livestock Husbandry

Abstract

Segmenting objects such as herd of cattle in natural and cluttered images is among the herculean dense prediction tasks of computer vision application to agriculture. To achieve the segmentation goal, we based the segmentation on the model of single objects by locations (SOLO) which is capable of exploiting the contextual cues and segmenting individual cattle by their locations and sizes. For its simple approach to instance segmentation with the use of instance categories, SOLO outperforms Mask R-CNN which uses detect-then-segment approach to predict a mask for each instance of cattle. The model is trained using synchronized stochastic gradient descent (SGD) over GPU to achieve a mAP of 0.94 making it 0.02 higher than the result recorded by the Mask R-CNN model. By using the focal loss, the proposed approach achieved 32.23 ADE on cattle contour detection making its performance better than the Mask R-CNN’s performance.