Related Study Based on Otsu Watershed Algorithm and New Squeeze-and-Excitation Networks for Segmentation and Level Classification of Tea Buds

摘要

In this study, image segmentation technology is utilized for segmentation of tea leaves and tender buds and deep learning technology is introduced for tea bud classification. Watershed algorithm has good robustness in the field of image segmentation under complex backgrounds, and the key of the algorithm is to determine the image segmentation threshold, which directly affects the accuracy of segmentation. “Maximum Between-Class Variance Method” (Otsu) as a great algorithm that can obtain the global optimal threshold is applied creatively to traditional watershed algorithm in this paper, which we call “Otsu Watershed Algorithm”. Then the structure of the “Squeeze-and-Excitation” (SE) block is adjusted appropriately to improve the feature presentation ability of the network by embedding into several common deep learning models. Extensive experiments demonstrate that this new SE block has superior accuracy and integration capability on challenging dataset and our tea bud dataset.