【摘要】 为解决焊接缺陷图像分割的结果出现失真、分割效果差的问题，以轮辋生产过程中的裂纹和气孔焊接缺陷图像为研究对象，提出了一种基于模拟退火(simulated annealing, SA)策略改进粒子群算法(improved particle swarm optimization, IPSO)的焊接缺陷三阈值图像分割方法。首先通过灰度值、平均灰度值和中值灰度值建立图像的三维最大类间方差(Otsu)模型；其次引入自适应惯性权重和非对称学习因子并融入SA策略增强算法求解效率和跳出局部最优的能力；最后利用SA-IPSO算法优化三维Otsu模型求解得到最佳阈值对应的缺陷分割图像。采用不同算法和模型对焊接缺陷图像进行分割，结果表明：对于裂纹和气孔焊接缺陷图像，本文算法在峰值信噪比(peak signal to noise ratio, PSNR)和结构相似性(structural similarity, SSIM)评价指标上均优于对比算法，在加快算法收敛的同时避免分割结果失真，提高了分割精度。更多还原

【Abstract】 To address the issues of distortion and poor segmentation results in weld defect image segmentation, the crack and porosity welding defect images in the rim production process were taken as the research object. An improved particle swarm optimization algorithm based on simulated annealing is proposed for the three-threshold image segmentation of welding defects. First, a three-dimensional Otsu model is constructed using the grayscale value, average grayscale value, and median grayscale value of the image. Next, an adaptive inertia weight and asymmetric learning factor were introduced and integrated into the SA strategy to enhance the algorithm’s solving efficiency and ability to escape local optima. Finally, the SA-IPSO algorithm was used to optimize the three-dimensional Otsu model to obtain the optimal threshold and corresponding defect segmentation image. Various algorithms and models are employed to segment welding defect images. The results show that for crack and porosity defect images, the proposed improved algorithm outperforms the comparison algorithms in terms of peak signal-to-noise ratio and structural similarity evaluation metrics. The proposed method accelerates algorithm convergence while preventing distortion in segmentation results, thereby improving segmentation accuracy.更多还原