Li Yihui, Ge Manling, Zhang Shiying, Wang Kaiwei
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China.
Hebei Key Laboratory of Electromagnetic Field and Electrical Reliability, Hebei University of Technology, Tianjin 300130, China.
Sensors (Basel). 2024 Feb 5;24(3):1031. doi: 10.3390/s24031031.
In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform.
为了实现小磁环表面细微缺陷图像的无监督分割，提高分割精度和计算效率，本文提出一种基于改进的多尺度多方向二维伽柏滤波器组的自适应阈值分割方法。首先，利用改进的多尺度多方向二维伽柏滤波器组对缺陷图像进行滤波降噪，抑制目标区域和背景区域内部的噪声污染，增强磁环缺陷与背景之间的差异。其次，分析处理后图像的灰度统计特征，根据图像的灰度统计规律构建分割阈值，实现小磁环表面细微缺陷图像的自适应分割。最后，设计基于BP神经网络的分类器，对不同阈值分割方法确定的疤痕图像和裂纹图像进行分类。迭代法、OTSU法、最大熵法和自适应阈值分割法的分类准确率分别为85%、87.5%、95%和97.5%。本文提出的自适应阈值分割方法分类准确率最高。通过验证和比较，所提算法能够快速准确地分割缺陷，有效抑制噪声干扰。在分割精度和计算效率方面均优于其他传统图像阈值分割方法。同时，在先进的SEED-DVS8168平台上对算法的实时性能进行了测试。
