Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm

作者：Li Wang, Kunming Jia, Yongmin Fu, Xiaoguang Xu, Lei Fan, Qiao Wang, Wenkui Zhu, Qunfeng Niu · 发表于：Frontiers in Plant Science · 年份：2023 · DOI：10.3389/fpls.2023.1108560 · 被引用次数：12 · 研究领域：Industrial Vision Systems and Defect Detection、Spectroscopy and Chemometric Analyses、Image Processing and 3D Reconstruction

Introduction: The classification of the four tobacco shred varieties, tobacco silk, cut stem, expanded tobacco silk, and reconstituted tobacco shred, and the subsequent determination of tobacco shred components, are the primary tasks involved in calculating the tobacco shred blending ratio. The identification accuracy and subsequent component area calculation error directly affect the composition determination and quality of the tobacco shred. However, tiny tobacco shreds have complex physical and morphological characteristics; in particular, there is substantial similarity between the expanded tobacco silk and tobacco silk varieties, and this complicates their classification. There must be a certain amount of overlap and stacking in the distribution of tobacco shreds on the actual tobacco quality inspection line. There are 24 types of overlap alone, not to mention the stacking phenomenon. Self-winding does not make it easier to distinguish such varieties from the overlapped types, posing significant difficulties for machine vision-based tobacco shred classification and component area calculation tasks. Methods: This study focuses on two significant challenges associated with identifying various types of overlapping tobacco shreds and acquiring overlapping regions to calculate overlapping areas. It develops a new segmentation model for tobacco shred images based on an improved Mask region-based convolutional neural network (RCNN). Mask RCNN is used as the segmentation network...