基于深度学习语义分割的导光板缺陷检测方法

当前导光板表面缺陷仍主要由人工肉眼观察进行检测, 仅有少数生产厂家利用传统的图像处理方法进行检测. 由于导光板缺陷在高分辨率工业相机拍摄的图像成像下仍极其微小, 且不同缺陷的特征各异, 以及整张导光板自身的导光点分布密集、不均匀等纹理特点, 导致传统的图像处理检测方法需要经验丰富的视觉工程师进行大量的特征提取算法编程工作和昂贵的代码维护成本, 准确率低且稳定性差, 为此提出一种基于深度学习语义分割的缺陷检测方法. 该方法通过训练神经网络的方式来自主学习提取导光板缺陷特征从而避免繁杂的特征提取算法编程工作. 首先, 对搜集的导光板缺陷进行缺陷标记, 制作样本集; 其次, 利用迁移学习将预先训练好的金字塔场景解析网络(PSPNet)对标记样本进行再训练; 进而, 利用训练好的模型实现对导光板缺陷的检测; 由于单独的深度学习语义分割缺陷检测方法通常无法满足工业实际应用需求, 最后还需结合简单的机器视觉方法, 对深度学习语义分割方法检出的所有疑似缺陷区域进行二次判断筛选. 实验结果表明, 该方法针对亮点、暗点和划痕3种缺陷的检出率高达96%, 基本可以满足工业检测要求.

Defect Detection Method of Light Guide Plate Based on Deep Learning Semantic Segmentation

At present, the defects on the surface of the light guide plate are mainly detected by human eye, only a few manufacturers use the traditional image processing methods. In the imaging of high-resolution industrial cameras, the defects of the light guide plate are still extremely small, the characteristics of different defects are different, and the light guide points of the entire light guide plate are densely distributed and uneven,which leads that the traditional image processing detection methods require experienced visual experts to carry out a large number of feature extraction algorithm programming work and expensive code maintenance cost, low accuracy and poor stability. Therefore, a defect detection method based on deep learning semantic segmentation is proposed. This method can learn and extract the characteristics of the light guide plate defects by training the neural network to avoid the complicated feature extraction algorithm programming. First, the collected light guide plate defects are marked for making a sample set. Secondly, the pre-trained Pyramid Scene Parsing Network (PSPNet) is used to retrain the labeled samples using transfer learning. Further, the trained model is used to achieve detection of defects of the light guide plate. Since the separate deep learning semantic segmentation defect detection method usually cannot meet the industrial practical application requirements, it is necessary to combine the simple machine vision method to make a second judgment and screening of all suspected defect regions detected by the deep learning semantic segmentation method. The experimental results show that the detection rate of the three defects of bright spots, dark spots, and scratches is as high as 96%, which can basically meet the industrial testing requirements.