Counting Livestock with Image Segmentation Neural Network

Abstract

Livestock farming industries, as well as almost any industry, want more and more data about the operation of their business and activities in order to make the right decisions. However, especially when considering very large animal farms, the precise and up-to-date information about the position and numbers of the animals is rather difficult to obtain. In this contribution, a novel engineering approach to livestock positioning and counting, based on image processing, is proposed. The approach is composed of two parts. Namely, a fully convolutional neural network for input image transformation, and a locator for animal positioning. The transformation process is designed in order to transform the original RGB image into a gray-scale image, where animal positions are highlighted as gradient circles. The locator then detects the positions of the circles in order to provide the positions of animals. The presented approach provides a precision rate of 0.9842 and a recall rate of 0.9911 with the testing set, which is, in combination with a rather suitable computational complexity, a good premise for the future implementation under real conditions.