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

IF 2.9 4区计算机科学 International Journal of Computational Intelligence Systems Pub Date : 2023-11-14 DOI:10.1007/s44196-023-00365-9

Jian Huang, Guangpeng Zhang, Li juan Ren, Nina Wang

引用次数: 0

Abstract

Abstract 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.

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基于YOLO5和全连通CRF的图像分割新方法

当手工抛光刀片时，熟练的工人可以通过观察抛光火花的特性来快速加工刀片。为了帮助工人更好地识别火花图像，我们使用了一个工业电荷耦合器件(CCD)相机来捕捉火花图像。首先用yolo5检测出火花图像区域，然后从背景中分割出来。其次，在全连通条件随机场(CRF)中对目标区域进行进一步分割和细化，得到完整的火花图像;实验结果表明，该方法可以快速、准确地分割整个火花图像。实验结果表明，该方法优于其他图像分割算法。该方法可以更好地分割不规则图像，提高火花图像的识别和分割效率，实现图像自动分割，取代人工观察。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Computational Intelligence Systems 工程技术-计算机：跨学科应用

自引率

3.40%

发文量

期刊介绍： The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: -Autonomous reasoning- Bio-informatics- Cloud computing- Condition monitoring- Data science- Data mining- Data visualization- Decision support systems- Fault diagnosis- Intelligent information retrieval- Human-machine interaction and interfaces- Image processing- Internet and networks- Noise analysis- Pattern recognition- Prediction systems- Power (nuclear) safety systems- Process and system control- Real-time systems- Risk analysis and safety-related issues- Robotics- Signal and image processing- IoT and smart environments- Systems integration- System control- System modelling and optimization- Telecommunications- Time series prediction- Warning systems- Virtual reality- Web intelligence- Deep learning