基于深度神经网络的表面划痕识别方法

为实现亮度不均的复杂纹理背景下表面划痕的鲁棒、精确、实时识别,提出一种基于深度神经网络的表面划痕识别方法。用于表面划痕识别的深度神经网络由风格迁移网络和聚焦卷积神经网络(CNN)构成,其中风格迁移网络针对亮度不均的复杂背景下的表面划痕进行预处理,风格迁移网络包括前馈转换网络和损失网络,首先通过损失网络提取亮度均匀模板的风格特征和检测图像的知觉特征,对前馈转换网络进行离线训练,获取网络最优参数值,最终使风格迁移网络生成亮度均匀且风格一致的图像,然后,利用所提出的基于聚焦结构的聚焦卷积神经网络对生成图像中的划痕特征进行提取并识别。以光照变化的金属表面为例,进行划痕识别实验,实验结果表明:与需要人工设计特征的传统图像处理方法及传统深度卷积神经网络相比,划痕漏报率低至8.54 %,并且收敛速度更快,收敛曲线更加平滑,在不同的深度模型下均可取得较好的检测效果,准确率提升2 %左右。风格迁移网络能够保留完整划痕特征的同时有效解决亮度不均的问题,从而提高划痕识别精度;同时聚焦卷积神经网络能够实现对划痕的鲁棒、精确、实时识别,大幅度降低划痕漏报率和误报率。

Surface scratch recognition method based on deep neural network

In order to achieve robust, accurate and real-time recognition of surface scratches under complex texture background with uneven brightness, a surface scratch recognition method based on deep neural network was proposed. The deep neural network for surface scratch recognition consisted of a style transfer network and a focus Convolutional Neural Network (CNN). The style transfer network was used to preprocess surface scratches under complex background with uneven brightness. The style transfer networks included a feedforward conversion network and a loss network. Firstly, the style features of uniform brightness template and the perceptual features of the detected image were extracted through the loss network, and the feedforward conversion network was trained offline to obtain the optimal parameter values of network. Then, the images with uniform brightness and uniform style were generated by style transfer network. Finally, the proposed focus convolutional neural network based on focus structure was used to extract and recognize scratch features in the generated image. Taking metal surface with light change as an example, the scratch recognition experiment was carried out. The experimental results show that compared with traditional image processing methods requiring artificial designed features and traditional deep convolutional neural network, the false negative rate of scratch detection is as low as 8.54% with faster convergence speed and smoother convergence curve, and the better detection results can be obtained under different depth models with accuracy increased of about 2%. The style transfer network can retain complete scratch features with the problem of uneven brightness solved, thus improving the accuracy of scratch recognition, while the focus convolutional neural network can achieve robust, accurate and real-time recognition of scratches, which greatly reduces false negative rate and false positive rate of scratches.