面向高光谱鱼类新鲜度分类的领域感知轻量型谱分组卷积
Domain-Aware Lightweight Spectral-Grouped Convolutions for Hyperspectral Fish Freshness Classification
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中文总结 AI 辅助
针对高光谱鱼类新鲜度分类的痛点，提出轻量型SGNet架构，结合分组卷积与双注意力机制，在自建数据集上实现高精度、低参数的新鲜度预测，参数远少于ResNet-50等模型。
中文摘要 AI 辅助
高光谱成像（HSI）可通过检测光谱带间的生化变化对鱼类新鲜度进行无损评估。然而，传统深度学习方法未充分解决高光谱数据的特定特性，例如光谱对空间纹理的主导性、有序标签结构以及少量训练样本。我们提出SGNet（谱分组网络，Spectral-Grouped Network），这是一种轻量型架构，利用分组卷积和深度空间通路分离光谱与空间特征提取。耦合通道挤压激励与空间门控的双注意力机制可自适应突出有效特征。在我们新构建的16天冷藏三文鱼鱼片数据集上测试时，SGNet仅用475万个参数便达到97.8%的分类准确率和0.64天的平均绝对误差（MAE）。 ablation研究验证了各组件的贡献，对比显示其参数量较ResNet-50和视觉Transformer降低5至18倍。研究表明，领域感知设计支持工业应用所需的精准、实时新鲜度预测。
英文摘要
Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands. However, conventional deep learning approaches do not fully address the particular characteristics of HSI data, such as spectral dominance over spatial textures, ordinal label structure, and a small number of training samples. We propose SGNet (Spectral-Grouped Network), a lightweight architecture that separates spectral and spatial feature extraction using grouped convolutions and a depthwise spatial pathway. A dual attention mechanism that couples channel-wise squeeze-and-excitation with spatial gating adaptively highlights informative features. SGNet achieves 97.8% classification accuracy and 0.64 days mean absolute error (MAE) with just 4.75M parameters when tested on our newly developed 16-day refrigerator-stored salmon fillet dataset. Ablation studies validate the contribution of each component, while comparisons demonstrate a five- to eighteen-fold parameter reduction relative to ResNet-50 and Vision Transformers. Our findings indicate that domain-aware design supports precise, real-time freshness prediction for industrial implementation.
发表机构
- Leeds Beckett University（利兹贝克特大学）
机构由 AI 辅助整理，请以论文原文为准。
