PDF(Pubmed)
Abstract：
A novel semisupervised hyperspectral imaging technique was developed to detect foreign materials (FMs) on raw poultry meat. Combining hyperspectral imaging and deep learning has shown promise in identifying food safety and quality attributes. However, the challenge lies in acquiring a large amount of accurately annotated/labeled data for model training. This paper proposes a novel semisupervised hyperspectral deep learning model based on a generative adversarial network, utilizing an improved 1D U-Net as its discriminator, to detect FMs on raw chicken breast fillets. The model was trained by using approximately 879,000 spectral responses from hyperspectral images of clean chicken breast fillets in the near-infrared wavelength range of 1000-1700 nm. Testing involved 30 different types of FMs commonly found in processing plants, prepared in two nominal sizes: 2 × 2 mm2 and 5 × 5 mm2. The FM-detection technique achieved impressive results at both the spectral pixel level and the foreign material object level. At the spectral pixel level, the model achieved a precision of 100%, a recall of over 93%, an F1 score of 96.8%, and a balanced accuracy of 96.9%. When combining the rich 1D spectral data with 2D spatial information, the FM-detection accuracy at the object level reached 96.5%. In summary, the impressive results obtained through this study demonstrate its effectiveness at accurately identifying and localizing FMs. Furthermore, the technique\'s potential for generalization and application to other agriculture and food-related domains highlights its broader significance.
摘要：
开发了一种新颖的半监督高光谱成像技术来检测生禽肉上的异物（FM）。结合高光谱成像和深度学习在识别食品安全和质量属性方面显示出希望。然而,挑战在于获取大量准确注释/标记的数据用于模型训练。本文提出了一种新的基于生成对抗网络的半监督高光谱深度学习模型,利用改进的1DU-Net作为鉴别器，检测生鸡胸肉片的FM。通过使用来自1000-1700nm近红外波长范围内的干净鸡胸肉片的高光谱图像的大约879,000光谱响应来训练该模型。测试涉及加工厂中常见的30种不同类型的FM，以两种标称尺寸制备:2×2mm2和5×5mm2。FM检测技术在光谱像素级和异物级均取得了令人印象深刻的效果。在光谱像素级别，该模型达到了100%的精度，超过93%的召回，F1得分为96.8%，和96.9%的平衡精度。当将丰富的一维光谱数据与二维空间信息相结合时，目标级别的FM检测精度达到96.5％。总之,通过这项研究获得的令人印象深刻的结果证明了其在准确识别和定位FMs方面的有效性。此外，该技术在其他农业和食品相关领域的推广和应用潜力凸显了其更广泛的意义。
