Livestock detection in aerial images using a fully convolutional network

• Main Authors: Liang Han, Pin Tao, Ralph R. Martin
• Format: Article
• Language: English
• Published: SpringerOpen 2019-03-01
• Series: Computational Visual Media
• Subjects: livestock detection; segmentation; classification
• Online Access: http://link.springer.com/article/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 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 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.