@article{Han2019, 
author = {Liang Han and Pin Tao and Ralph R. Martin},
title = {Livestock detection in aerial images using a fully convolutional network},
year = {2019},
journal = {Computational Visual Media},
volume = {5},
number = {2},
pages = {221-228},
keywords = {segmentation, livestock detection, classi-fication},
url = {https://www.sciopen.com/article/10.1007/s41095-019-0132-5},
doi = {10.1007/s41095-019-0132-5},
abstract = {In order to accurately count the number of animals grazing on grassland, we present a livestock detection algorithm using modified versions of U-net and Google Inception-v4 net. This method works wellto detect dense and touching instances. We also introduce a dataset for livestock detection in aerial images, consisting of 89 aerial images collected by quadcopter. Each image has resolution of about  3000×4000 pixels, and contains livestock with varying shapes, scales, and orientations.We evaluate our method by comparison against Faster RCNN and Yolo-v3 algorithms using our aerial livestock dataset. The average precision of our method is better than Yolo-v3 and is comparable to Faster RCNN.}
}