livestock detection, segmentation, classi-fication
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.
Tsinghua University Press
Liang Han, Pin Tao, Ralph R. Martin. Livestock detection in aerial images using a fully convolutional network. Computational Visual Media 2019, 5(2): 221-228.