TY - JOUR
T1 - Detecting sheep in UAV images
AU - Sarwar, Farah
AU - Griffin, Anthony
AU - Rehman, Saeed Ur
AU - Pasang, Timotius
PY - 2021/8
Y1 - 2021/8
N2 - In the last decade, researchers have focused more on deep convolutional neural networks (CNNs) than other machine learning algorithms for object detection, localization, classification and segmentation. Such CNNs have achieved remarkable results in these fields and use the bounding boxes as the ground truth data. In this research article, we have used a fully connected network (FCN) for livestock detection in aerial images captured by an unmanned aerial vehicle (UAV), that used centroids as ground truth data. For performance evaluation and comparison, we have proposed a single-layered and a seven-layered CNN network in this article. These proposed networks are trained using state-of-the-art method, Region-based CNN. In addition, AlexNet, GoogLeNet, VGG16, VGG19 and ResNet50 were also fine-tuned for livestock detection. The results of the FCN and one of our proposed networks are then merged to improve the recall of the complete system from 90% to 98%.
AB - In the last decade, researchers have focused more on deep convolutional neural networks (CNNs) than other machine learning algorithms for object detection, localization, classification and segmentation. Such CNNs have achieved remarkable results in these fields and use the bounding boxes as the ground truth data. In this research article, we have used a fully connected network (FCN) for livestock detection in aerial images captured by an unmanned aerial vehicle (UAV), that used centroids as ground truth data. For performance evaluation and comparison, we have proposed a single-layered and a seven-layered CNN network in this article. These proposed networks are trained using state-of-the-art method, Region-based CNN. In addition, AlexNet, GoogLeNet, VGG16, VGG19 and ResNet50 were also fine-tuned for livestock detection. The results of the FCN and one of our proposed networks are then merged to improve the recall of the complete system from 90% to 98%.
KW - Deep learning
KW - Object detection
KW - Livestock
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85107703965&partnerID=8YFLogxK
U2 - 10.1016/j.compag.2021.106219
DO - 10.1016/j.compag.2021.106219
M3 - Article
AN - SCOPUS:85107703965
VL - 187
JO - COMPUTERS AND ELECTRONICS IN AGRICULTURE
JF - COMPUTERS AND ELECTRONICS IN AGRICULTURE
SN - 0168-1699
M1 - 106219
ER -