用高光谱成像自动识别猪肉片上的异物，提升食品安全检测精度。
Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly
- 结合预处理与轻量级视觉变换器进行像素级分割
- 在900-1700nm波段下实现高精度异物识别
- 适合工业场景中实时质检，抗噪声和温度干扰
食品加工行业保障食品安全与质量至关重要，污染物检测仍是长期挑战。本研究提出一种基于高光谱成像（HSI）的自动化方法，用于检测猪五花肉表面的异物。利用近红外（NIR）波段（900–1700 nm）的高光谱相机获取数据，有效识别传统视觉方法难以察觉的污染物。该方案融合预处理技术与基于轻量级视觉变换器（ViT）的分割方法，可准确区分污染物、肉类、脂肪及传送带材料。所提策略在检测精度与训练效率方面表现优异，同时应对了工业应用中的固有噪声、温差变化以及污染物与肉质间光谱相似性等关键问题。实验结果验证了高光谱成像在提升食品安全方面的有效性，展现出其在自动化质量控制中广泛实时应用的潜力。
原文摘要 · Abstract (English)
Ensuring food safety and quality is critical in the food processing industry, where the detection of contaminants remains a persistent challenge. This study presents an automated solution for detecting foreign objects on pork belly meat using hyperspectral imaging (HSI). A hyperspectral camera was used to capture data across various bands in the near-infrared (NIR) spectrum (900-1700 nm), enabling accurate identification of contaminants that are often undetectable through traditional visual inspection methods. The proposed solution combines pre-processing techniques with a segmentation approach based on a lightweight Vision Transformer (ViT) to distinguish contaminants from meat, fat, and conveyor belt materials. The adopted strategy demonstrates high detection accuracy and training efficiency, while also addressing key industrial challenges such as inherent noise, temperature variations, and spectral similarity between contaminants and pork belly. Experimental results validate the effectiveness of hyperspectral imaging in enhancing food safety, highlighting its potential for broad real-time applications in automated quality control processes.
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