Beef Carcass Grading using Deep Convolutional Networks
Resumo
Beef carcass grading is an invaluable tool to ensure meat quality. In most of the Brazilian abattoirs, carcasses are graded through visual analysis by trained graders. In order to automate this process, we evaluate seven image deep learning models. For this purpose, a new dataset was created containing images of 670 bovine half-carcasses taken during regular operation in an abattoir. The images were graded by three professionals. All three experts agreed in only 9.9% of the cases, and two out of three graders agreed in 58.82%. The graders disagreed on 31.28% of the images. These results indicate the complexity of the problem. Nonetheless, an overall accuracy of 53% was achieved using convolutional neural networks, which is close to human performance, when the agreement between the graders is considered. Furthermore, an accuracy of around 91% can be achieved if the cases of disagreement are disregarded.
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S. Piao, T. Okura, and M. Irie, “On-site evaluation of wagyu beef carcasses based on the monounsaturated, oleic, and saturated fatty acid composition using a handheld fiber-optic near-infrared spectrometer,” Meat Science, vol. 137, pp. 258 – 264, 2018.
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M. Chmiel, M. Słowiński, and K. Dasiewicz, “Application of computer vision systems for estimation of fat content in poultry meat,” Food Control, vol. 22, no. 8, pp. 1424 – 1427, 2011.
M. Alcayde, F. Elijorde, and Y. Byun, “Quality monitoring system for pork meat using computer vision,” in 2019 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), 2019, pp. 1–7.
J. Araújo, A. Lima, M. Nunes, M. Sousa, G. Serrão, E. Morais, L. Daher, and A. Silva, “Relationships among carcass shape, tissue composition, primal cuts and meat quality traits in lambs: A pls path modeling approach,” Small Ruminant Research, vol. 182, pp. 52 – 66, 2020.
D. L. Pinto, A. Selli, D. Tulpan, L. T. Andrietta, P. L. M. Garbossa, G. V. Voort, J. Munro, M. McMorris, A. A. C. Alves, R. Carvalheiro, M. D. Poleti, J. C. de Carvalho Balieiro, and R. V. Ventura, “Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms,” Livestock Science, vol. 267, p. 105152, 2023.
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G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017, p. 4278–4284.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z.Wojna, “Rethinking the inception architecture for computer vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818–2826.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations, 2015.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1800–1807.
E. C. Tetila, “A deep learning approach for automatic counting of soybean insect pests,” IEEE Geoscience and Remote Sensing Letters, 2019.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV), 2015.
Publicado
13/11/2023
Como Citar
MENEZES, Geazy Vilharva et al.
Beef Carcass Grading using Deep Convolutional Networks. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 30-35.
DOI: https://doi.org/10.5753/wvc.2023.27528.
