Woody breast (WB) is a myopathy in modern broiler chickens that causes the breast muscle to become unusually stiff and fibrous, leading to decreased meat quality and significant economic losses. State-of-the-art automated WB detection relies on a side-view imaging system to analyze the bending behavior of a single fillet as it falls off a conveyor belt. While highly accurate, this approach is constrained by its single-fillet field of view, creating throughput bottlenecks on commercial processing lines. In this paper, we address this limitation via a novel multi-fillet detection architecture utilizing a top-down camera configuration. To validate our approach, we first develop a high-fidelity digital twin of an industrial conveyor system. Next, we synthesize a diverse dataset of 3D fillet meshes and model their viscoelastic bending dynamics using a physics-based simulation engine. Lastly, a continuous 2D shape deformation score is extracted from the top-down perspective as the simulated fillets traverse the roller precipice. Experimental results demonstrate that the top-down shape score effectively captures the contour changes of the fillets as it bends, providing a robust and scalable alternative to a side-view imaging system for simultaneous multi-fillet WB evaluation.

翻译：木质化鸡胸肉（WB）是现代肉鸡的一种肌病，会导致胸肌异常僵硬和纤维化，进而降低肉质并造成重大经济损失。当前最先进的自动化WB检测依赖于侧视成像系统，通过分析单片鸡胸肉从传送带跌落时的弯曲行为进行判断。尽管该方法精度很高，但受限于单片肉的视野范围，在商业加工线上造成了吞吐量瓶颈。本文通过一种采用俯视相机配置的新型多片肉检测架构来解决这一局限。为验证我们的方法，首先构建了一个高保真的工业传送系统数字孪生模型。随后，合成了一个包含多样化三维肉片网格的数据集，并利用基于物理的仿真引擎对其粘弹性弯曲动力学进行建模。最后，从俯视视角提取了连续的二维形状形变分数，用以表征仿真肉片通过滚筒边缘时的状态。实验结果表明，俯视角度的形状分数能有效捕捉肉片弯曲时的轮廓变化，为侧视成像系统提供了一种稳健且可扩展的替代方案，适用于同时评估多片肉的WB状况。