基于深度学习的多模型融合图像智能检测方法研究

Research on intelligent detection method of multi-model fusion image based on deep learning

传统的Faster R-CNN定位算法中应用的是通过最近邻插值算法进行插值的RoiPooling,对于小缺陷的识别效果不佳,本文将其改进为使用双线性插值算法的RoiAlign,提高了轮胎异常检测的精确度。针对传统的轮胎缺陷样本检测面临特征提取困难的问题,通过将ResNet和DenseNet两个网络模型进行融合搭建起的RSDC-Net网络模型,提高了网络的泛化、感知能力,增强了特征提取能力,而且将该网络应用于深度学习的可解释性研究中,实现了深度学习的可视化。目前神经元分类的研究领域还有很大空缺,所以为了针对敏感区域图像结果进行潜层的神经元分类研究,本文设计出双卷积门限循环神经网络来作为网络模型来完成神经元的分类研究,该网络模型在4种模型对比实验中表现最佳。

The traditional Faster R-CNN positioning algorithm uses RoiPooling,which is interpolated by the nearest neighbor interpolation algorithm.The recognition effect of small defects is not good.This article will improve it to RoiAlign using bilinear interpolation algorithm,which improves tire abnormality detection.Accuracy.Traditional tire defect sample detection faces the problem of difficult feature extraction.The RSDC-Net(resnet and densenet converged network)network model built by fusing the two network models of ResNet and DenseNet has improved the generalization and perception capabilities of the network.Enhance the feature extraction ability,and apply the network to the interpretability research of deep learning,and realize the visualization of deep learning.At present,there is still a big gap in the research field of neuron classification.Therefore,in order to carry out the research on the neuron classification of the latent layer according to the image results of the sensitive area,this paper designs a double convolution threshold recurrent neural network as a network model to complete the neuron classification,the network model performed best in the four model comparison experiments.