Salient object detection of dairy goats in farm image based on background and foreground priors

Abstract

In order to achieve object detection of dairy goats in farm image efficiently and accurately, this paper proposes a novel salient object detection approach based on background and foreground priors. First, we improve the FastMBD algorithm to generate background-prior-based saliency map, which eliminate background interference initially. Second, the approach of seed selection is optimized to determine the foreground seeds more accurately, and then saliency map is generated based on manifold ranking to further eliminate the background interference. Finally, two saliency maps are fused, and then combined with post-processing to achieve saliency object detection of farm image. Subsequently, we also use the threshold segmentation based on K-Means algorithm to separate the objects and background. Moreover, the horizontal scanning method and contour extraction are employed to realize the extraction and counting of dairy goats. Experimental results show that the proposed approach based on background and foreground priors has promising performance, and the accuracy of the proposed segmentation method was up to 89.503%.