We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于改进OTSU法的原棉破籽类杂质快速检测.
- Authors
齐英兰
- Abstract
In view of the difficulty in determining the optimal threshold of OTSU method in visual detection of raw cotton seed broken impurities, an impurity detection method based on particle swarm optimization was proposed. Firstly, the image is transformed into gray scale to remove redundant information and noise data. Then, the inter-class variance was set as the objective function model, and the optimization was carried out by dynamically adjusting the inertia weight factor and the adaptive dynamic adjustment learning factor. The global optimal position of the whole population and the historical optimal position of the particle itself were iterated, and the maximum inter-class variance was solved to obtain the optimal image segmentation threshold. The experimental results show that compared with OTSU method, the relative error of the improved OTSU method is reduced by 2.74% on average. The detected impurity edge information can effectively characterize the impurity pixel attributes, and the description of impurity edge is clear, which is suitable for the rapid detection of raw cotton broken seed impurities.
- Subjects
PARTICLE swarm optimization; IMAGE segmentation; SET functions; PIXELS; NOISE
- Publication
Wool Textile Journal, 2022, Vol 50, Issue 6, p90
- ISSN
1003-1456
- Publication type
Article
- DOI
10.19333/j.mfkj.20220105705