Incremental two-network approach to develop a purity analyzer system for canola seeds

Abstract

Given a suitable dataset, transfer learning using deep convolutional neural networks is an effective method to develop a system to detect and classify objects. Despite having models pretrained on large general-purpose datasets, the requirement to manually label an application-specific dataset remains a limiting factor in system development. We consider this wider problem in the context of the purity analysis of canola seeds, where end users wish to distinguish species of interest from contaminants in images taken with optical microscopes. We use a Detector network, trained only to detect seeds, to help label the dataset used to train an Analyzer network, capable of both seed detection and classification. We present results, over three experiments that involve 25 contaminant species, including Primary and Secondary Noxious Weed Seeds (as per the Canadian Weed Seeds Order), to validate our incremental approach. We also compare the proposed system to competing ones in a literature review.