基于Gabor变换和极限学习机的铝箔密封性检测

Aluminum Foil Sealing Seal Detection Based on Gabor Transformation and Extreme Learning Machine

针对铝箔封口温度场分布特征进行了研究,提出了一种基于Gabor变换和极限学习机(ELM)的封口密封性检测方法。对采集到的不同特征类型热像图进行Gabor变换,提取纹理特征训练极限学习机神经网络。然后利用训练结果对热像图进行分类识别,通过热像图分类特征判断铝箔封口密封情况。与提取颜色特征的BP神经网络对比分析发现,基于Gabor变换和极限学习机的算法具有泛化性强、响应速度快、精度高等优势。

After the research on the distribution characteristic of temperature field for aluminum seal , a kind of inspection method for the sealing based on Gabor transformation and extreme learning machine (ELM) was proposed.The Gabor transformation was conducted on various thermal images ,and the textural features are extracted to train the ELM neural network.Then the training results are used to classify and identify the thermal images, and the sealing of aluminum seal could be judged through the classification features of the thermal images.Compared and analysed with BP neural network which extracts color features,it is found that the algorithm based on Gabor transformation and ELM has the advantages of strong generalization,fast response speed,high precision,etc.