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一种图像处理技术,用于优化工业缺陷检测,使用dehazing算法

Xuanyi Zhao¹, Xiaohan Dou¹, Gengpei Zhang¹

¹Yangtze University, Jingzhou, Hubei, China.

|May 2, 2025

概括

本研究介绍了一种工业缺陷检测算法,使用脱雾技术来改善雾条件下的图像质量. 改进的方法显著提高了检测准确度,减少了错误,超过了现有模型的性能.

科学领域:

计算机视觉计算机视觉
图像处理图像处理
工业自动化工业自动化

背景情况:

工业缺陷检测至关重要,但由于水雾等环境因素的图像退化而受到挑战.
现有的算法与模糊图像作斗争,导致检测难度和错误增加.

研究的目的:

开发和验证一个工业缺陷检测算法,增强了除尘技术.
提高在复杂,雾影响的工业环境中检测缺陷的准确性和可靠性.

主要方法:

对在水雾中捕获的工业图像应用了一种优化的脱气处理方法.
增强图像与改进的YOLOv8模型一起用于缺陷检测.
使用峰值信号与噪声比率 (PSNR) 和结构相似性指数测量 (SSIM) 评估了性能.

主要成果:

该方法实现了平均PSNR的34.9dB和SSIM的0.951.
与卷积神经网络 (CNN) 和MADNet模型相比,拟议的算法显示出更高的性能.
改进的YOLOv8模型显著提高了缺陷检测的可靠性,并减少了错过的检测.

结论:

综合的除尘和缺陷检测方法有效地解决了水雾引起的图像质量问题.
这种新的方法为在不利条件下检测工业缺陷提供了强大的解决方案.
该技术显示出可转移到其他应用程序的潜力,例如在充满烟雾的环境中进行搜索和救援.

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