Detecting sheep in UAV images

Farah Sarwar, Anthony Griffin, Saeed Ur Rehman, Timotius Pasang

Research output: Contribution to journalArticlepeer-review

Abstract

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%.

Original languageEnglish
Article number106219
Number of pages12
JournalCOMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume187
DOIs
Publication statusPublished - Aug 2021

Keywords

  • Deep learning
  • Object detection
  • Livestock
  • UAV

Fingerprint

Dive into the research topics of 'Detecting sheep in UAV images'. Together they form a unique fingerprint.

Cite this