基于深度学习的焊缝缺陷X射线检测图像识别与增强

Image recognition and enhancement of X-ray detection of weld defects based on deep learning

为了提高焊缝缺陷X射线图像识别的准确率,需要采用有效的图像增强技术,笔者研究了不同图像增强方法对焊缝图像质量的影响,用峰值信噪比、结构相似度、结构清晰度、信息熵等参数对图像增强质量进行评价。试验结果表明,直方图均衡化(HE)与限制对比度自适应直方图均衡化(CLAHE)有较好的对比度增强效果,非局部均值滤波(NLM)与小波降噪(DWT)的去噪综合表现较好。基于CLAHE-NLM的图像增强处理可以更有效地帮助深度学习模型进行焊缝缺陷分类识别,焊缝缺陷分类的准确率与F1值达97.6%和96.93%,相较于未增强处理的数据集提高了3.2%与5.23%。

In order to enhance the accuracy of X-ray image recognition for weld seam defects,it is essential to employ effective image enhancement techniques.this study investigates investigated the impact of different image enhancement techniques on the quality of weld seam images.The parametersEvaluation metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index(SSIM),Structural Clarity,and Information Entropy wereare employed to assess the quality of image enhancement.Experimental results indicated that histogram equalization(HE)and contrast-limited adaptive histogram equalization(CLAHE)exhibited superior contrast enhancement effects,while non-local means filtering(NLM)and discrete wavelet transform(DWT)performed well in noise reduction.Image enhancement processing based on CLAHE-NLM proveds to be more effective in assisting deep learning models for weld seam defect classification and recognition.The accuracy and F1 score of weld seam defect classification reached 97.6%and 96.93%,respectively,representing an improvement of 3.2%and 5.23%compared to the data set without enhancement preprocessing.