Livestock detection in aerial images using a fully convolutional network

作     者：Liang Han Pin Tao Ralph R.Martin 

作者机构：Department of Computer Technology and ApplicationQinghai UniversityXiningChina Department of Computer Science and TechnologyTsinghua UniversityBeijingChina School of Computer Science and InformaticsCardiff UniversityCardiffWalesUK 

出 版 物：《Computational Visual Media》 (计算可视媒体（英文版）)

年 卷 期：2019年第5卷第2期

页      面：221-228页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Scientific and Technological Achievements Transformation Project of Qinghai, China (Project No. 2018-SF-110) the National Natural Science Foundation of China (Projects Nos. 61866031 and 61862053) 

主　　题：livestock detection segmentation classification 

摘      要：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 well to 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 *** 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.