期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：4
语种：English
出版社：IAENG - International Association of Engineers
摘要：The applications of deep learning to livestock farming have in recent years gained wide acceptance from the computer vision community due to the continuous achievement of its applications to agricultural tasks. Moreover, the essentiality of deep learning is its practicality in detecting, segmenting, and classifying video and image objects without which precision livestock farming would have been impossible. However, the applications of most of the state-of-the-art models of deep learning to multiple cow objects image segmentation are not accurate and cannot generate colorimetric information due to poor pre-processing mechanism inherent in the associated methods and unequal training of their backbone layers. To overcome the abovementioned limitations, an enhanced deep learning framework of Mask Region-based Convolutional Neural Network (Mask R-CNN) based on Generalized Color Fourier Descriptors (GCFD) is proposed. The enhanced model produced 0.93 mean Average Precision (mAP). The result shows the performance capability of the proposed framework over the state-of-the-art models for cow image segmentation.
