论文标题
叶异常检测和定位的深度学习方法
A deep learning approach for detection and localization of leaf anomalies
论文作者
论文摘要
通常通过诉诸监督深度学习方法来自动化农作物中可能的疾病的检测和定位。在这项工作中，我们通过将三种不同类型的自动编码器应用于健康，不健康的胡椒和樱桃叶图像的特定开源数据集，以无监督的模型来解决这些目标。 CAE，CVAE和VQ-VAE自动编码器被部署以筛选此类数据集的未标记图像，并根据图像重建，删除异常，检测和定位进行比较。相对于所有这些目标，矢量定量的变分结构是表现最好的架构。
The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches. In this work, we tackle these goals with unsupervised models, by applying three different types of autoencoders to a specific open-source dataset of healthy and unhealthy pepper and cherry leaf images. CAE, CVAE and VQ-VAE autoencoders are deployed to screen unlabeled images of such a dataset, and compared in terms of image reconstruction, anomaly removal, detection and localization. The vector-quantized variational architecture turns out to be the best performing one with respect to all these targets.
