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- Title
Enhanced Deep Learning Framework for Cow Image Segmentation.
- Authors
Bello, Rotimi-Williams; Azlan Mohamed, Ahmad Sufril; Talib, Abdullah Zawawi; Olubummo, Daniel A.; Enuma, O. Charles
- Abstract
The applications of deep learning to livestock farming have in recent years gained wide acceptance from the computer vision community due to the continuous achievement of its applications to agricultural tasks. Moreover, the essentiality of deep learning is its practicality in detecting, segmenting, and classifying video and image objects without which precision livestock farming would have been impossible. However, the applications of most of the state-of-the-art models of deep learning to multiple cow objects image segmentation are not accurate and cannot generate colorimetric information due to poor pre-processing mechanism inherent in the associated methods and unequal training of their backbone layers. To overcome the abovementioned limitations, an enhanced deep learning framework of Mask Region-based Convolutional Neural Network (Mask R-CNN) based on Generalized Color Fourier Descriptors (GCFD) is proposed. The enhanced model produced 0.93 mean Average Precision (mAP). The result shows the performance capability of the proposed framework over the state-of-the-art models for cow image segmentation.
- Subjects
DEEP learning; IMAGE segmentation; COMPUTER vision; CONVOLUTIONAL neural networks; COWS; LIVESTOCK farms
- Publication
IAENG International Journal of Computer Science, 2021, Vol 48, Issue 4, p1182
- ISSN
1819-656X
- Publication type
Article