Deep Learning Approach for Automatic Segmentation of Dirt on Cattle Skin using Image Data

Syed Mohammed Shamsul Islam, Syed Afaq Ali Shah, Chau Duc Minh Nguyen

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

Free-range cattle live and graze in soil and muddy pasture. This natural habitat easily makes the skin of cattle dirty and can lead to several issues not only for cattle but also for their raisers/farmers, who also have to meet certain requirements of hygienic meat and dairy products for consumers. Some of these issues include: (1) the stress experienced by cattle during the manual cleaning process by raisers, (2) the high labour cost of cleanliness and (3) the high amount of time required to clean a large number of cattle in a farm. These issues raise a need to develop an automatic and reliable cleanliness system to detect and segment dirt on the skin of cattle. In this paper, we propose a deep learning-based technique, called Deep Segmentation Network, to accomplish this challenging task. Our proposed deep learning-based system relies on image data to detect and segment dirt on the skin of cattle. We also propose baseline methods, which use ResNet50 and ResNet101 as the backbone models. We performed extensive experiments to validate the proposed approaches for the segmentation of dirt on cattle skin. Our experimental results demonstrate the superior performance of the proposed technique.

Original languageEnglish
Title of host publicationProceedings of the 2023 38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023
EditorsDonald Bailey, Amal Punchihewa, Abhipray Paturkar
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9798350370515
DOIs
Publication statusPublished - 12 Dec 2023
Event38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023 - Palmerston North, New Zealand
Duration: 29 Nov 202330 Nov 2023

Publication series

NameInternational Conference Image and Vision Computing New Zealand
ISSN (Print)2151-2191
ISSN (Electronic)2151-2205

Conference

Conference38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023
Country/TerritoryNew Zealand
CityPalmerston North
Period29/11/2330/11/23

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