A robust machine vision system for body measurements of beef calves
Measuring body dimensions is a useful method of assessing the health and growth of young beef cattle. However, performing these measurements in a barn environment can present a number of unique challenges. The objective of this paper is to design an image capture system and image processing algorith...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/4699b7e965c6446e81f774b76db64abc |
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| Sumario: | Measuring body dimensions is a useful method of assessing the health and growth of young beef cattle. However, performing these measurements in a barn environment can present a number of unique challenges. The objective of this paper is to design an image capture system and image processing algorithm that can meet these challenges. The system uses two RGB-D cameras to collect images from the top-left and top-right of the calf. Images were collected in a barn environment along with ground truth body measurements. Colour image processing was used to remove the background by making use of a deep learning instance segmentation model for each camera. The segmented data from the two cameras was registered to create a 3D image of the calf, which was then used to measure a few key body dimensions. The experimental results showed a mean error of 0.2 cm for heart girth, -0.8 cm for withers height, -0.2 cm for midpiece height, and -2.1 cm for pin height. |
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