Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model

Syariah Compliance Automated Chicken Processing System (SYCUT) is a system for monitoring the slaughtering process to ensure that chickens are slaughtered in accordance with sharia of Islam. SYCUT uses vision inspection technology to determine whether slaughtered chickens are halal or otherwise. The...

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Main Author:	Nor Muhammad, Nor Aziah Amirah
Format:	Thesis
Language:	English
Published:	2022
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/99648/1/NorAziahAmirahMMJIIT2022.pdf
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id	my-utm-ep.99648
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spelling	my-utm-ep.996482023-03-08T04:17:56Z Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model 2022 Nor Muhammad, Nor Aziah Amirah T Technology (General) Syariah Compliance Automated Chicken Processing System (SYCUT) is a system for monitoring the slaughtering process to ensure that chickens are slaughtered in accordance with sharia of Islam. SYCUT uses vision inspection technology to determine whether slaughtered chickens are halal or otherwise. The vision inspection technology consists of a detection module to detect whether the esophagus of chickens is cut accordingly. The researcher employed Viola-Jones object detection framework to train the detection module. The detection module had problems due to images of esophagus that were bloodied, blurred, or occluded. This resulted in a low detection rate in the system. Besides, a conventional method requires image preprocessing tool like low-pass filter and Otsu’s thresholding to improve the conditions of the images before detection which adds to the computational cost. In this study, the researcher divided image inputs into categories to reduce misclassification and aid in data annotation. Then, the researcher proposed a poultry esophagus detection system based on deep learning to improve the current algorithm in SYCUT. The researcher combined the deep learning method with the RetinaNet and Mask R-CNN models, which could perform segmentation and object detection in a single image. The researcher then compared the proposed method with the previous conventional SYCUT algorithm. The proposed method could detect bloodied and occluded images more accurately. The developed algorithm improves overall esophageal detection performance from 68.65 to 92.77 per cent. The SYCUT performs efficiently even in uncontrolled working environments due to the effectiveness of the developed deep learning method. However, the limitation of this deep learning method is it needs huge data for training. This research only improves the detection of certain image types, like bloodied and occluded. Future work should include improving the precision-recall value of the system and its real-time implementation for esophageal detection in real or simulated environments. 2022 Thesis http://eprints.utm.my/id/eprint/99648/ http://eprints.utm.my/id/eprint/99648/1/NorAziahAmirahMMJIIT2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150836 masters Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology (MJIIT)
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Nor Muhammad, Nor Aziah Amirah Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model
description	Syariah Compliance Automated Chicken Processing System (SYCUT) is a system for monitoring the slaughtering process to ensure that chickens are slaughtered in accordance with sharia of Islam. SYCUT uses vision inspection technology to determine whether slaughtered chickens are halal or otherwise. The vision inspection technology consists of a detection module to detect whether the esophagus of chickens is cut accordingly. The researcher employed Viola-Jones object detection framework to train the detection module. The detection module had problems due to images of esophagus that were bloodied, blurred, or occluded. This resulted in a low detection rate in the system. Besides, a conventional method requires image preprocessing tool like low-pass filter and Otsu’s thresholding to improve the conditions of the images before detection which adds to the computational cost. In this study, the researcher divided image inputs into categories to reduce misclassification and aid in data annotation. Then, the researcher proposed a poultry esophagus detection system based on deep learning to improve the current algorithm in SYCUT. The researcher combined the deep learning method with the RetinaNet and Mask R-CNN models, which could perform segmentation and object detection in a single image. The researcher then compared the proposed method with the previous conventional SYCUT algorithm. The proposed method could detect bloodied and occluded images more accurately. The developed algorithm improves overall esophageal detection performance from 68.65 to 92.77 per cent. The SYCUT performs efficiently even in uncontrolled working environments due to the effectiveness of the developed deep learning method. However, the limitation of this deep learning method is it needs huge data for training. This research only improves the detection of certain image types, like bloodied and occluded. Future work should include improving the precision-recall value of the system and its real-time implementation for esophageal detection in real or simulated environments.
format	Thesis
qualification_level	Master's degree
author	Nor Muhammad, Nor Aziah Amirah
author_facet	Nor Muhammad, Nor Aziah Amirah
author_sort	Nor Muhammad, Nor Aziah Amirah
title	Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model
title_short	Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model
title_full	Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model
title_fullStr	Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model
title_full_unstemmed	Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model
title_sort	poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model
granting_institution	Universiti Teknologi Malaysia
granting_department	Malaysia-Japan International Institute of Technology (MJIIT)
publishDate	2022
url	http://eprints.utm.my/id/eprint/99648/1/NorAziahAmirahMMJIIT2022.pdf
_version_	1776100630372286464

Poultry esophagus detection using retinanet and mask region-based convolutional neural network object detection model

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