基于深度学习的车载导航导光板表面缺陷检测研究

针对车载导航导光板表面缺陷像素值分布不均且普遍较小、背景复杂多变等特点,提出了基于改进掩膜区域卷积神经网络(Mask Region-based Convolutional Neural Network,Mask R-CNN)模型检测车载导航导光板表面缺陷的检测方法.首先,引入PinFPN模块改进原有Mask R-CNN...

Research on Surface Defect Detection of Vehicle Navigation Light Guide Plate based on Deep Learning

Aiming at the uneven and generally small pixel value distribution and changeable background of surface defects of vehicle navigation light guide plate,this paper proposes a detection method for surface defects of vehicle navigation light guide plate based on improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model.Firstly,PinFPN module is introduced to improve the feature fusion network of the original Mask R-CNN,and high and low semantic information is fully used to form a shared feature layer with both semantic and location information at all levels,so to improve the detection accuracy of the overall network.Secondly,the introduction of skip connection structure and SE(Sequence and Excitation)module improves segmentation branches of the network and insufficient acquisition of semantic information in traditional Mask R-CNN network.Finally,a series of comparative experiments are performed on the self-built data set of the vehicle navigation light guide plate,which proves that the proposed method has the advantages in detection accuracy and segmentation.The detection accuracy on the self-built data set reaches 95.3%,which meets the requirements of industrial detection.