Visual Image Analysis for a new classification method of bovine carcasses according to EU legislation criteria

Negretti, P.; Bianconi, Giovanna; Cannata, Giovanni; Catillo, G.; Steri, Roberto; Barrasso, Roberta; Bozzo, Giancarlo

doi:10.1016/j.meatsci.2021.108654

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Advancements in digital imaging technology, coupled with evidence of bias in human graders, prompted legislative changes in the early 2000s, which allowed abattoirs to adopt an automated EUROP grading system based on video image analysis (VIA) [19] . Segura et al [20] evaluated the application of two CVSs in predicting the composition of primal and retail cuts in youthful beef carcasses. Negretti et al [21] developed an application software utilizing visual image analysis, which aimed to facilitate the SEUROP and Fat Cover grading of bovine carcasses.…”

Section: Discussionmentioning

confidence: 99%

A comparative analysis of carcass and meat traits of yaks

Ma,

Kong

et al. 2023

Food Science of Animal Products

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This study primarily compares the carcass and meat quality traits of yaks of different ages and genders. Measurements of carcass weight and characteristics were taken for male and female Gannan yaks aged 1-2, 2-4, 4-6, and 6 years and older. Additionally, the weight and meat quality of 9 major cuts were measured. The results indicated significant variations in various aspects of yak carcasses around the age of 4, and substantial differences between male and female yaks after the age of 4. Different body regions showed distinct meat quality. The tenderness of the tenderloin is the highest (P < 0.05), while the striploin is the lowest (P < 0.05), and the protein content of the hindquarter cuts is higher. Principal component analysis (PCA) was employed to identify key factors significantly affecting total meat yield. Subsequently, predictive models for total meat yield were developed for yaks aged 1-4 years, 4+ years for males, and 4+ years for females based on these factors. Furthermore, the meat from the 9 major cuts was classified according to its intrinsic meat quality traits, which holds significant reference value for the assessment of yak carcass yield grades and the grading of quality in meat cuts, ultimately contributing to the realization of the full potential of the yak meat industry.

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Section: Discussionmentioning

confidence: 99%

A comparative analysis of carcass and meat traits of yaks

Ma,

Kong

et al. 2023

Food Science of Animal Products

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“…The semantic information and orientation information of ancient architectural decoration image have certain randomness, so the deep and shallow feature weights change constantly with the image content; Moreover, the decoration image of ancient buildings will change with the passage of time, which increases the probability of problems such as blurred boundary, uneven illumination and high noise in the visual image [13], which brings great difficulty to the subsequent processing and application of the image. Therefore, in order to balance the feature weights of different levels of neural network and improve the image quality of ancient architectural decoration, the residual network and cavity convolution are integrated into the neural network to construct the cavity neural network, so as to remove the network gradient and fully extract the multi-scale features of the image.…”

Section: Neural Networkmentioning

confidence: 99%

Filtering and Enhancement Method of Ancient Architectural Decoration Image based on Neural Network

Wang¹

2022

IJACSA

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Due to poor ambient light or uneven lighting, the old decoration image acquisition methods are easy to cause the image blur. To solve this problem, this paper proposes a neural network-based filtering enhancement method for ancient architectural decoration images, which preserves image details by enhancing contrast, smoothing noise reduction and edge sharpening. Based on the convolutional neural network which is composed of encoder, decoder and layer hop connection, the residual network and hole convolution are introduced, and the hole U-Net neural network is constructed to fuse the pixel feature blocks of different levels. This method enhanced the image contrast according to the gray level and frequency histogram, and aiming at the gray value of the pixel to be processed in the image. And the middle value of the gray value of the neighborhood pixel is used to filter the noise of the ancient building decoration image. The paper also analyzes the joint strength of beams and columns in ancient buildings, and calculates the elastic constants of beams and columns and the stress at the joint of them, considering the image texture characteristics of the wood in ancient buildings with the mortise and tenon connection of beams and columns. Experimental results show that the proposed method has good noise suppression performance, can effectively obtain image detail features, and significantly improve the subjective visual effect of ancient architectural decoration images.

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“…Research regarding assistive systems for bovine carcass grading has been conducted in different regions, such as the US and Europe. Negretti et al [14], for instance, proposed one such high-performance system for the evaluation of carcass conformation and fat cover in accordance to the European legislation (specifically, the system was developed in Italy). However, the proposed system is actually semi-automatic, requiring that the user indicates some reference points in the image to be used in the process of evaluation.…”

Section: Introductionmentioning

confidence: 99%

Beef Carcass Grading using Deep Convolutional Networks

Menezes,

Tetila,

Gonçalves

et al. 2023

Anais Do XVIII Workshop De Visão Computacional (WVC 2023)

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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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Visual Image Analysis for a new classification method of bovine carcasses according to EU legislation criteria

Cited by 5 publications

References 17 publications

A comparative analysis of carcass and meat traits of yaks

A comparative analysis of carcass and meat traits of yaks

Filtering and Enhancement Method of Ancient Architectural Decoration Image based on Neural Network

Beef Carcass Grading using Deep Convolutional Networks

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