A New Image Segmentation Method Based on the YOLO5 and Fully Connected CRF

When manually polishing blades, skilled workers can quickly machine a blade by observing the characteristics of the polishing sparks. To help workers better recognize spark images, we used an industrial charge-coupled device (CCD) camera to capture the spark images. Firstly, the spark image region d...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computational intelligence systems 2023-11, Vol.16 (1), p.1-10, Article 180
Main Authors: Huang, Jian, Zhang, Guangpeng, Ren, Li juan, Wang, Nina
Format: Article
Language:English
Subjects:
Artificial Intelligence
CCD
Computational Intelligence
Control
Engineering
Fully connected CRF
Image segmentation
Mathematical Logic and Foundations
Mechatronics
Research Article
Robotics
Spark image
YOLO5
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:When manually polishing blades, skilled workers can quickly machine a blade by observing the characteristics of the polishing sparks. To help workers better recognize spark images, we used an industrial charge-coupled device (CCD) camera to capture the spark images. Firstly, the spark image region detected by yolo5, then segment from the background. Secondly, the target region was further segmented and refined in a fully connected conditional random field (CRF), from which the complete spark image obtained. Experimental results showed that this method could quickly and accurately segment whole spark image. The test results showed that this method was better than other image segmentation algorithms. Our method could better segment irregular image, improve recognition and segmentation efficiency of spark image, achieve automatic image segmentation, and replace human observation.
ISSN:1875-6883
1875-6883
DOI:10.1007/s44196-023-00365-9