用嗅觉视觉融合模型提升稻米劣化检测精度与效率
Feature Recalibration Based Olfactory-Visual Multimodal Model for Enhanced Rice Deterioration Detection
- 通过特征重校准机制增强细微劣化特征的表达能力
- 相比SS-Net准确率提升8.67%，平均优于传统模型11.51%
- 无需高成本设备，适合田间快速检测与农业推广
多模态方法广泛用于稻米劣化检测，但对细粒度异常特征的表征和提取能力有限。且依赖高成本设备如高光谱相机和质谱仪，增加检测成本并延长数据采集时间。为此，本文提出一种基于特征重校准的嗅觉-视觉多模态模型，以提升稻米劣化检测效果。提出细粒度劣化嵌入构建器（FDEC）重构带标签的多模态嵌入数据集，增强样本表征；提出细粒度劣化重校准注意力网络（FDRA-Net），突出信号变化，提升对稻米表面细微劣化的敏感性。实验表明，该方法相比SS-Net分类准确率提升8.67%，平均优于其他传统基线模型11.51%，同时简化检测流程。田间实测结果表明其在精度与操作简便性方面均具优势，可拓展至农业与食品工业中的其他农食应用。
原文摘要 · Abstract (English)
Multimodal methods are widely used in rice deterioration detection, but they exhibit limited capability in representing and extracting fine-grained abnormal features. Moreover, these methods rely on devices such as hyperspectral cameras and mass spectrometers, which increase detection costs and prolong data acquisition time. To address these issues, we propose a feature recalibration based olfactory-visual multimodal model for enhanced rice deterioration detection. A fine-grained deterioration embedding constructor (FDEC) is proposed to reconstruct the labeled multimodal embedded feature dataset, thereby enhancing sample representation. A fine-grained deterioration recalibration attention network (FDRA-Net) is proposed to emphasize signal variations and improve sensitivity to fine-grained deterioration on the rice surface. Compared with SS-Net, the proposed method improves classification accuracy by 8.67%, with an average improvement of 11.51% over other traditional baseline models, while simultaneously simplifying the detection procedure. Furthermore, field detection results demonstrate advantages in both accuracy and operational simplicity. The proposed method can also be extended to other agrifood applications in agriculture and the food industry.
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