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 remarkable 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 pre-trained model and source code of CarcassFormer is available for research purposes at: \url{https://github.com/UARK-AICV/CarcassFormer}.

翻译：在食品工业中，加工过程中评估家禽胴体质量是至关重要的一步。本研究提出了一种无需熟练劳动力或检验员参与的胴体质量自动化评估有效方法。所提出的系统基于机器学习（ML）与计算机视觉（CV）技术，能够实现缺陷自动检测及胴体质量评估。为此，我们引入了一种名为CarcassFormer的端到端框架，该框架基于Transformer架构构建，旨在有效提取视觉表征的同时，同步检测、分割并分类家禽胴体缺陷。本框架能够分析因生产及运输福利问题，以及加工厂致昏机、烫毛机、拔毛机及其他设备故障所导致的缺陷。为对框架进行基准测试，我们首先采集了一个包含7,321张图像的数据集，每张图像中同时包含单个及多个胴体。在本研究中，我们将CarcassFormer系统的性能与当前最先进（SOTA）方法在分类、检测及分割任务上进行了比较。通过大量定量实验，本框架始终优于现有方法，在AP、AP@50和AP@75等各项评估指标上均展现出显著提升。此外，定性结果凸显了CarcassFormer在捕捉羽毛等细节、高精度定位与分割胴体方面的优势。为促进进一步研究与协作，CarcassFormer的预训练模型及源代码已面向研究用途开放，获取链接为：\url{https://github.com/UARK-AICV/CarcassFormer}。