Abstract：
Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380-1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.
摘要：
快速，对有缺陷的玉米粒进行有效和无损的检测对于其在粮仓中的高质量存储至关重要。提出了基于光谱和空间注意的高光谱成像(HSI)与卷积神经网络(CNN)相结合的方法(Spl-Spal-At)模块,用于不同类型玉米籽粒的识别。HSI数据在380-1000纳米范围内的六类发芽，热损伤,昆虫受损，发霉,收集破碎和健康的内核。CNN-Spl-At，基于光谱建立了CNN-Spal-At和CNN-Spl-Spal-At模型，图像及其融合特征作为识别不同内核的输入。通过支持向量机（SVM）和极限学习机（ELM）建立了模型，进一步比较了所提出模型和常规模型的性能。结果表明，注意序列模型对CNN的识别能力明显优于SVM和ELM模型，融合特征比单一特征更有利于表达不同核的外观。而CNN-Spl-Spal-At模型对训练集和测试集的平均分类准确率分别为98.04%和94.56%,分别。识别结果直观地呈现在具有不同颜色的内核的表面图像上。本研究建立的CNN-Spl-Spal-At模型可以有效检测缺陷玉米粒,它也有很大的潜力，为开发基于HSI技术的玉米品质无损检测设备提供分析方法。
