CarcassFormer: an end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect.

Autor:	Tran M; Department of Computer Science and Computer Engineering, 1 University of Arkansas, Fayetteville, AR 72701, USA., Truong S; Department of Computer Science and Computer Engineering, 1 University of Arkansas, Fayetteville, AR 72701, USA., Fernandes AFA; Cobb Vantress, Inc, Siloam Springs, AR 72761, USA., Kidd MT; Department of Poultry Science, Fayetteville, AR 72701, USA., Le N; Department of Computer Science and Computer Engineering, 1 University of Arkansas, Fayetteville, AR 72701, USA. Electronic address: thile@uark.edu.
Jazyk:	angličtina
Zdroj:	Poultry science [Poult Sci] 2024 Aug; Vol. 103 (8), pp. 103765. Date of Electronic Publication: 2024 May 21.
DOI:	10.1016/j.psj.2024.103765
Abstrakt:	In the food industry, assessing the quality of poultry carcasses during processing is a crucial step. This study proposes an effective approach for automating the assessment of carcass quality without requiring skilled labor or inspector involvement. The proposed system is based on machine learning (ML) and computer vision (CV) techniques, enabling automated defect detection and carcass quality assessment. To this end, an end-to-end framework called CarcassFormer is introduced. It is built upon a Transformer-based architecture designed to effectively extract visual representations while simultaneously detecting, segmenting, and classifying poultry carcass defects. Our proposed framework is capable of analyzing imperfections resulting from production and transport welfare issues, as well as processing plant stunner, scalder, picker, and other equipment malfunctions. To benchmark the framework, a dataset of 7,321 images was initially acquired, which contained both single and multiple carcasses per image. In this study, the performance of the CarcassFormer system is compared with other state-of-the-art (SOTA) approaches for both classification, detection, and segmentation tasks. Through extensive quantitative experiments, our framework consistently outperforms existing methods, demonstrating re- markable improvements across various evaluation metrics such as AP, AP@50, and AP@75. Furthermore, the qualitative results highlight the strengths of CarcassFormer in capturing fine details, including feathers, and accurately localizing and segmenting carcasses with high precision. To facilitate further research and collaboration, the source code and trained models will be made publicly available upon acceptance. Competing Interests: DISCLOSURES The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: uark.edu; cobbvantress.com (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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