Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network

This paper proposes a solution to localization and classification of rice grains in an image. All existing related works rely on conventional based machine learning approaches. However, those techniques do not do well for the problem designed in this paper, due to the high similarities between different types of rice grains. The deep learning based solution is developed in the proposed solution. It contains pre-processing steps of data annotation using the watershed algorithm, auto-alignment using the major axis orientation, and image enhancement using the contrast-limited adaptive histogram equalization (CLAHE) technique. Then, the mask region-based convolutional neural networks (R-CNN) is trained to localize and classify rice grains in an input image. The performance is enhanced by using the transfer learning and the dropout regularization for overfitting prevention. The proposed method is validated using many scenarios of experiments, reported in the forms of mean average precision (mAP) and a confusion matrix. It achieves above 80% mAP for main scenarios in the experiments. It is also shown to perform outstanding, when compared to human experts.