A new efficient multiobject detection and size calculation for blended tobacco shred using an improved YOLOv7 network and LWC algorithm

作者：Li Wang, Kunming Jia, Qunfeng Niu, Yang Niu, Wentao Ma · 发表于：Research Square · 年份：2023 · DOI：10.21203/rs.3.rs-3279283/v1 · 研究领域：Industrial Vision Systems and Defect Detection、Advanced Neural Network Applications、Spectroscopy and Chemometric Analyses

Abstract Detection of the four tobacco shred varieties, including tobacco silk, cut stem, expended tobacco silk, and reconstituted tobacco shred, and the subsequent calculation of the tobacco shred component ratio and unbroken tobacco shred rate are the primary tasks in cigarette inspection lines. The accuracy, speed and recognizable complexity of tobacco shred images affect the feasibility of practical applications directly in the inspection line field. In cigarette quality inspection lines, there are bound to be a large number of single tobacco shreds and a certain amount of overlapped tobacco shreds at the same time, and it is especially critical to identify both single and overlapped tobacco shreds at once, that is, fast blended tobacco shred detection based on multiple targets. However, it is difficult to classify tiny single tobacco shreds with complex morphological characteristics, not to mention classifying and locating tobacco shreds with 24 types of overlap alone, which poses significant difficulties for machine vision-based blended tobacco shred multiobject detection and unbroken tobacco shred rate calculation tasks. This study focuses on the two challenges of identifying blended tobacco shreds with single tobacco shreds and overlapped tobacco simultaneously in the field application and calculating the unbroken tobacco shred rate. In this paper, a new multiobject detection model is developed for blended tobacco shred images based on an improved YOLOv7-tiny. YOLOv7-...