HyperNut: Hyper Spectral Dataset of Nuts for Unsupervised Defect Detection and Segmentation

Abstract

Hyperspectral Imaging (HSI), providing detailed information from various spectrums, is a suitable candidate for detecting defects in real-world applications, which is a hot topic in the field of computer vision nowadays. We introduce the HyperNut dataset, containing hyperspectral images of almonds and pistachios in the visible and near-infrared (VIS-NIR) ranges (400nm-1000nm). This dataset contains non-anomalous samples that can be used for training unsupervised approaches and defective samples for testing purposes. To our best knowledge, our dataset is the only one in the literature that (a) allows a thorough analysis of nuts quality by providing different types of defective samples, (b) provides real-world samples containing multiple objects and considering noise and variable environmental conditions while sampling, and (c) allows defect segmentation by providing masks presenting exact locations of defects in samples. Moreover, we have tested basic and simple anomaly detection methods on the hyperspectral data and the related RGB images and compared the results to show that hyperspectral images are suitable candidates for defect detection problems.