基于卷积神经网络的PCB缺陷图像识别

PCB缺陷图像检测是确保PCB生产质量的重要环节,但传统的人工PCB缺陷检测具有劳动强度大、工作效率低等不足.为此,本文研究了一种基于卷积神经网络的PCB缺陷图像识别方法,建立了包括三种PCB缺陷和无缺陷图像的数据集,基于ResNet101网络模型搭建了PCB缺陷图像识别分类模型.引入迁移学习的方法,基于在大数据集上充...     

基于卷积神经网络的烟火智能识别算法应用研究

事实证明,将深度学习与烟火检测结合所得智能算法,具有成本低、智能水平高以及覆盖范围大的优点,通常只需借助普通相机便能够做到精准检测并识别不同形态的烟火,现已在很多领域得到推广。该文以该算法的应用为研究重点,在对研究背景、算法理论进行介绍的基础上,详细说明了模型建立过程,并对该模型所具有可靠性进行了检测。结果表明,该算法所具有平均精度较传统算法提高了约5.5%,对该算法加以应用可使烟火预警以及险情跟踪等工作得到高效开展,确保人员得到及时救助,与此同时,相关机构对事故灾害进行管理的能力也会得到显著提升。     

基于改进一维卷积神经网络的滚动轴承故障识别

滚动轴承的故障识别对于防止旋转机械系统故障恶化并保证其安全运行具有重要意义。针对现有智能诊断模型参数多、识别效率低的问题,提出一种基于改进一维卷积神经网络的滚动轴承故障识别(FRICNN–1D)方法。通过引入1×1卷积核增强一维卷积神经网络模型的非线性表达能力;并用全局平局池化层代替传统卷积神经(CNN)网络中的全连接层,以降低模型参数和计算量,且防止过拟合现象。试验结果表明,该方法可以准确识别滚动轴承不同故障状态,具有一定的工程实际应用潜力。     

基于浅层卷积神经网络的面部表情识别

面部表情识别在人机交互等领域中发挥着重要作用,采用深度学习方法提高了模型精度,但过深的网络加大了计算成本,造成识别滞后和性能低下的问题.本文提出了一种浅层卷积神经网络模型,它受到Xception网络的启发,结合了残差模块和深度可分离卷积,同时引入了功能模块与网络进行融合.微调后的网络结构简单、模型较小,在FER2013...     

基于环形特征的卷积神经网络轮毂识别

针对实际生产中不同种类轮毂的混流生产问题,提出了一种基于环形特征的卷积神经网络轮毂识别算法。将直角坐标下的环形轮毂映射到极坐标中,归一化为标准形式的矩形,提取轮毂图像的环形特征信息,减少冗余特征产生的影响;设计了一种改进的VGG网络架构,利用深度可分离卷积打破输出通道维度与卷积核大小的联系,在不损失网络性能的同时降低了计算量,能够在实际生产中轮毂识别任务在有限的算力情况下实时进行计算;从有效性和实时性两个方面对轮毂识别算法进行评估,且通过Inception V3、SVM、KNN等模型的对比实验,验证了该算法可以实时地对轮毂自适应分类。实验表明: 该方法对轮毂图像的处理精度达到99%以上,单幅图像平均处理时间降低至11.78ms。     

基于跨层卷积神经网络的石刻碑文识别

石刻碑文的研究具有重要的历史价值和文化价值,但是由于受到自然环境的风化、腐蚀和人为的破坏,碑刻文字存在很大的识别难度.传统的识别方法通过选择特征的提取方式,得到笔画、部首等浅层特征,准确率不高.卷积神经网络可以利用深层网络自动提取到更抽象的特征用于识别,具有更高的准确率.本文针对碑刻数据收集困难,数据量较少的问题,提出了一种基于VGG-16的跨层卷积神经网络进行石刻碑文的识别,使用投影分割改进法实现碑文的分割,该方法在本文构建的测试数据集上对石刻碑文的识别具有很好的效果.     

基于小波散射卷积神经网络的结构损伤识别

损伤识别是结构状态评估领域的关键问题之一,对确保结构安全性有重要意义。深度学习算法在基于振动的结构损伤识别方面带来了许多突破,但从海量数据中挖掘结构损伤关键信息仍是亟待解决的技术难题。该研究提出了基于一维卷积神经网络(one-dimensional-convolutional neural network, 1D-CNN)深度学习的结构多类型损伤识别模型,采用小波散射变换对1D-CNN架构第一层卷积滤波器进行替换,通过散射系数实现输入层原始数据降维与特征提取,结合CNN卷积层、激活层和池化层实现监测数据特征增强处理。在此基础上,结合1D-CNN全连接层与Softmax函数实现特征数据分类,从而实现结构多类型损伤定位与定量高效识别。通过钢桁架结构和斜拉桥两种数值模型对上述框架进行了验证。结果表明：与普通卷积神经网络模型相比,基于小波散射卷积神经网络的结构损伤识别精度显著提升,损伤分类准确率达95.0%以上。随着传感数据环境噪声比例的增加,小波散射卷积神经网络损伤分类准确率虽略有下降,但仍保持较高精准度,说明该方法具有较强的鲁棒性抗噪能力。     

基于卷积神经网络和Transformer网络的鸟声识别

针对传统鸟声识别算法中特征提取方式单一、分类识别准确率低等问题,提出一种结合卷积神经网络和Transformer网络的鸟声识别方法。该方法综合考虑网络局部特征学习和全局上下文依赖性构造,从原始鸟声音频信号中提取短时傅里叶变换(Short Time Fourier Transform,STFT)语谱图特征,将其输入到卷积神经网络(ConvolutionalNeural Network,CNN)中提取局部频谱特征信息,同时提取鸟声信号的对数梅尔特征及一阶差分、二阶差分特征用于合成梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)混合特征向量,将其输入到Transformer网络中获取全局序列特征信息,最后融合所提取的特征可得到更丰富的鸟声特征参数,通过Softmax分类器得到鸟声识别结果。在Birdsdata和xeno-canto鸟声数据集上进行实验,平均识别准确率分别达到了97.81%和89.47%。实验结果表明该方法相较于其他现有的鸟声识别模型具有更高的识别准确率。     

基于卷积神经网络的食品塑料包装袋光谱识别

目的实现食品塑料包装袋的快速检测和材质区分。方法研究使用高光谱成像技术在450～950nm波长范围下采集了49组不同食品包装袋样本的光谱数据,利用Savitzky-Golay平滑滤波、数据归一化和主成分分析进行预处理,建立决策树、支持向量机2种传统机器学习模型和卷积神经网络模型,并比较了它们对包装袋材质的识别性能。结果决策树模型与支持向量机模型的验证识别率分别为87.8%和88.9%,卷积神经网络模型的验证识别率高达100%,损失函数值最终下降到0.0171且达到收敛,在分类效果和精度上具有明显的优势。结论高光谱检测方法不破坏检材,重现性好,稳定性强,实现了对食品塑料包装袋的精准识别。卷积神经网络模型对食品包装袋高光谱数据的识别效果最好,为食品包装袋质量检测领域中塑料包装袋的识别鉴定提供依据。     

Mango Leaf Disease Identification Using Fully Resolution Convolutional Network

Due to the high demand for mango and being the king of all fruits, it is the need of the hour to curb its diseases to fetch high returns. Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms. Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases, i.e., Anthracnose, apical-necrosis, etc., of a mango plant leaf. To solve this issue, we proposed a CNN based Fully-convolutional-network (FrCNnet) model for the segmentation of the diseased part of the mango leaf. The proposed FrCNnet directly learns the features of each pixel of the input data after applying some preprocessing techniques. We evaluated the proposed FrCNnet on the real-time dataset provided by the mango research institute, Multan, Pakistan. To evaluate the proposed model results, we compared the segmentation performance with the available state-of-the-art models, i.e., Vgg16, Vgg-19, and Unet. Furthermore, the proposed model's segmentation accuracy is 99.2% with a false negative rate (FNR) of 0.8%, which is much higher than the other models. We have concluded that by using a FrCNnet, the input image could learn better features that are more prominent and much specific, resulting in an improved and better segmentation performance and diseases’ identification. Accordingly, an automated approach helps pathologists and mango growers detect and identify those diseases.     

Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

Melanoma, also called malignant melanoma, is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells, which give the skin its color. Melanoma is one of the skin diseases, which is exceptionally and globally dangerous, Skin lesions are considered to be a serious disease. Dermoscopy-based early recognition and detection procedure is fundamental for melanoma treatment. Early detection of melanoma using dermoscopy images improves survival rates significantly. At the same time, well-experienced dermatologists dominate the precision of diagnosis. However, precise melanoma recognition is incredibly hard due to several factors: low contrast between lesions and surrounding skin, visual similarity between melanoma and non-melanoma lesions, and so on. Thus, reliable automatic detection of skin tumors is critical for pathologists’ effectiveness and precision. To take care of this issue, numerous research centers around the world are creating autonomous image processing-oriented frameworks. We suggested deep learning methods in this article to address significant tasks that have emerged in the field of skin lesion image processing: we provided a Convolutional Neural Network (CNN) based framework using an Inception-v3 (INCP-v3) melanoma detection scheme and accomplished very high precision (98.96%) against melanoma detection. The classification framework of CNN is created utilizing TensorFlow and Keras in the backend (in Python). It likewise utilizes Transfer-Learning (TL) approach. It is prepared on the data gathered from the “International Skin Imaging Collaboration (ISIC)” repositories. The experiments show that the suggested technique outperforms state-of-the-art methods in terms of predictive performance.     

基于卷积神经网络的印刷套准识别方法

目的 针对目前印刷套准识别方法依赖于经验人工设计特征提取的问题,提出一种不需要人工提取图像特征的卷积神经网络模型,实现印刷套准状态的识别.方法 采用图像增强技术实现不均衡训练集的均衡化,增加训练集图像的数量,提高模型的识别准确率.设计基于AlexNet网络结构的印刷套准识别模型的结构参数,分析批处理样本数量和基础学习率对模型性能的影响规律.结果 文中方法获得的总印刷套准识别准确率为0.9860,召回率为1.0000,分类准确率几何平均数为0.9869.结论 文中方法能自动提取图像特征,不依赖于人工设计的特征提取方法.在构造的数据集上,文中方法的分类性能优于实验中的支持向量机方法.     

Sentiment Classification Based on Piecewise Pooling Convolutional Neural Network

Recently, the effectiveness of neural networks, especially convolutional neural networks, has been validated in the field of natural language processing, in which, sentiment classification for online reviews is an important and challenging task. Existing convolutional neural networks extract important features of sentences without local features or the feature sequence. Thus, these models do not perform well, especially for transition sentences. To this end, we propose a Piecewise Pooling Convolutional Neural Network (PPCNN) for sentiment classification. Firstly, with a sentence presented by word vectors, convolution operation is introduced to obtain the convolution feature map vectors. Secondly, these vectors are segmented according to the positions of transition words in sentences. Thirdly, the most significant feature of each local segment is extracted using max pooling mechanism, and then the different aspects of features can be extracted. Specifically, the relative sequence of these features is preserved. Finally, after processed by the dropout algorithm, the softmax classifier is trained for sentiment classification. Experimental results show that the proposed method PPCNN is effective and superior to other baseline methods, especially for datasets with transition sentences.     

基于卷积神经网络的印刷品颜色缺陷检测

目的 提取样本图像颜色直方图特征对卷积神经网络进行训练,达到快速、高准确率检测图像颜色缺陷的目的.方法 将标准图像从RGB颜色空间转换至HSV颜色空间,通过改变图像H,S,V三分量值获取训练样本和测试样本;在HSV颜色空间中非均匀量化图像的颜色直方图,得到所有训练样本和测试样本的颜色直方图特征;利用样本图像颜色直方图特征训练卷积神经网络,然后对测试样本进行检测,研究检测的速度、准确率,并将该检测方法与逐像素、超像素、BP神经网络和支持向量机方法进行对比.结果 对于图片尺寸为512×512的彩色图像,卷积神经网络检测单幅图片的平均检测时间约为57.66 ms,训练样本图像为50000张时,卷积神经网络方法对10000张测试样本进行检测的准确率为99.77％.结论 卷积神经网络方法在保证高准确率的前提下大幅提高检测精度,对于印刷品色差缺陷在线检测具有良好的应用价值.     

Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

Weather phenomenon recognition plays an important role in the field of meteorology. Nowadays, weather radars and weathers sensor have been widely used for weather recognition. However, given the high cost in deploying and maintaining the devices, it is difficult to apply them to intensive weather phenomenon recognition. Moreover, advanced machine learning models such as Convolutional Neural Networks (CNNs) have shown a lot of promise in meteorology, but these models also require intensive computation and large memory, which make it difficult to use them in reality. In practice, lightweight models are often used to solve such problems. However, lightweight models often result in significant performance losses. To this end, after taking a deep dive into a large number of lightweight models and summarizing their shortcomings, we propose a novel lightweight CNNs model which is constructed based on new building blocks. The experimental results show that the model proposed in this paper has comparable performance with the mainstream non-lightweight model while also saving 25 times of memory consumption. Such memory reduction is even better than that of existing lightweight models.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.