计算机视觉 (Computer Vision)

Deep Computer Vision is capable of doing object detection and image classification task. In image classification tasks, the particular system receives some input image and the system is aware of some predetermined set of categories or labels. There are some fixed set of category labels and the job of the computer is to look at the picture and assign it a fixed category label. Convolutional Neural Network (CNN) has gained wide popularity in the field of pattern recognition and machine learning. In our present work, we have constructed a Convolutional Neural Network (CNN) for the identification of the presence of tantalum and niobium fragments in a High Entropy Alloy (HEA). The results showed 100 % accuracy while testing the given dataset.

深度计算机视觉能够执行对象检测和图像分类任务。 在图像分类任务中，特定系统接收一些输入图像，并且系统知道一些预定的类别或标签集。 有一些固定的类别标签集，计算机的工作是看图片并为其分配一个固定的类别标签。 卷积神经网络(CNN)在模式识别和机器学习领域获得了广泛的普及。 在我们目前的工作中，我们构建了卷积神经网络(CNN)，用于识别高熵合金(HEA)中钽和铌碎片的存在。 测试给定数据集时，结果显示100％的准确性。

Introduction

介绍

Vision is the most important sense that humans possess. In day to day life, people depend on vision for example identifying objects, picking objects, navigation, recognizing complex human emotions and behaviors. Deep computer vision is able to solve extraordinary complex tasks that were not able to be solved in the past. Facial detection and recognition and detection are an example of deep computer vision. Figure 1 shows the vision coming into a deep neural network in the form of images or pixels or videos and the output at the bottom is the depiction of a human face [1–4].

视觉是人类拥有的最重要的感觉。 在日常生活中，人们依靠视觉来识别物体，拾取物体，导航，识别复杂的人类情感和行为。 深入的计算机视觉能够解决过去无法解决的非凡复杂任务。 面部检测，识别和检测是深度计算机视觉的一个示例。 图1显示了以图像，像素或视频形式进入深层神经网络的视觉，底部的输出是对人脸的描绘[1-4]。

The next thing should be worth answering to the question, how computer process an image or a video, and how do they process pixels coming from those? The images are just numbers and also the pixels have some numerical values. So our image can be represented by a two-dimensional matrix consisting of numbers. Let’s understand this with an example of image identification i.e. whether the image is of a boy or a girl or an animal. Figure 2 shows that the output variable takes a class label and can produce a probability of belonging to a particular class.

接下来的事情应该值得回答这个问题：计算机如何处理图像或视频，以及它们如何处理来自这些图像或视频的像素？ 图像只是数字，像素也有一些数值。 因此，我们的图像可以由包含数字的二维矩阵表示。 让我们以图像识别的示例(即图像是男孩还是女孩还是动物)来理解这一点。 图2显示了输出变量带有类别标签，并且可以产生属于特定类别的概率。

In order to properly classify the image, our pipeline must correctly tell about what is unique about the particular picture. Convolutional Neural Network (CNN) finds application in the manufacturing and material science domain. Lee et al. [5] proposed a CNN model for fault diagnosis and classification in the manufacturing process of semiconductors. Weimer et al. [6] designed deep convolutional neural network architectures for automated feature extraction in industrial applications. Scime et al. [7] used the CNN model for the detection of in situ processing defects in laser powder bed fusion additive manufacturing. The results showed that the CNN architecture improved the classification accuracy and overall flexibility of the designed system.

为了正确分类图像，我们的管道必须正确告知特定图片的独特之处。 卷积神经网络(CNN)在制造和材料科学领域得到了应用。 Lee等。 [5]提出了一种用于半导体制造过程中故障诊断和分类的CNN模型。 Weimer等。 [6]设计了用于工业应用中自动特征提取的深度卷积神经网络体系结构。 Scime等。 [7]使用CNN模型来检测激光粉末床熔融增材制造中的原位加工缺陷。 结果表明，CNN体系结构提高了设计系统的分类准确性和整体灵活性。

In the present work, we have designed the CNN architecture for detecting the trace of tantalum and niobium in the microstructure of high entropy alloy (HEA). In 1995, Yeh et al. [8] firstly discovered the high entropy alloys, and in 2004 Cantor et al. [9] coined high entropy alloy as a multi-component system. HEAs are generally advanced alloys and novel alloys which are consist of 5–35 at.% where all the elements behave as principal elements. In comparison to their conventional alloys, they possess superior properties like high wear, corrosion resistance, high thermal stability, and high strength. Zhang et al. [10–11] listed down the various parameters for the parameters for fabrication of HEAs which are shown in the below equations:

在当前的工作中，我们设计了CNN体系结构，用于检测高熵合金(HEA)微观结构中的痕量钽和铌。 1995年，Yeh等人。 [8]首先发现了高熵合金，2004年Cantor等人。 [9]创造了高熵合金作为多组分系统。 HEA通常是高级合金和新型合金，由5–35 at。％的成分组成，其中所有元素均作为主要元素。 与常规合金相比，它们具有优异的性能，如高耐磨性，耐腐蚀性，高热稳定性和高强度。 张等。 [10-11]列出了制造HEA的各种参数，这些参数如下式所示：

HEAs find application in various industries like aerospace, submarines, automobiles, and nuclear power plant industries [12–14]. HEAs are also used as a filler material for the micro-joining process [15]. Geanta et al. [16] carried out the testing and characterization of HEAs from AlCrFeCoNi System for Military Applications. It was observed that at the melt state, the microstructure of HEAs has frozen appearance as shown in Figure 3.

HEA在航空航天，潜艇，汽车和核电厂等各种行业中都有应用[12-14]。 HEA还用作微连接过程的填充材料[15]。 Geanta等。 [16]进行了军事应用AlCrFeCoNi系统的HEA的测试和表征。 观察到，在熔融状态下，HEA的微观结构具有冻结外观，如图3所示。

Material and Methods

材料与方法

Geanta et al. [17] fabricated biocompatible FeTaNbTiZrMo HEAs. In our study, we have used microstructure data from their research. The obtained microstructure is shown in Figures 4 and 5. Data collection is the process of gathering and measuring information from countless different sources. In order to use the data we collect to develop practical artificial intelligence (AI) and machine learning solutions, it must be collected and stored in a way that makes sense for the business problem at hand. Since we had a shortage of images, so we first did Image Augmentation.

Geanta等。 [17]制作了生物相容的FeTaNbTiZrMo HEA。 在我们的研究中，我们使用了他们研究的微观结构数据。 获得的微观结构如图4和5所示。数据收集是从无数不同来源收集和测量信息的过程。 为了使用我们收集的数据来开发实用的人工智能(AI)和机器学习解决方案，必须以对眼前的业务问题有意义的方式来收集和存储数据。 由于图像不足，因此我们首先进行图像增强。

Image data augmentation is used to expand the training dataset in order to improve the performance and ability of the model to generalize. Image data augmentation is supported in the Keras deep learning library via the Image Data Generator class. So, input data consists of two images. As we know that we can’t train our deep neural network with only two images because that would result in the over-fitting of the model. Over-fitting a model basically means that our model will give the best score on training data but not on testing or validation data or the data that it has not seen before. So such an over-fitted model will be of no use to train our model effectively, we will make more images with the help of these input images. We will achieve this by Image Augmentation.

图像数据增强用于扩展训练数据集，以提高模型的性能和泛化能力。 Keras深度学习库通过图像数据生成器类支持图像数据增强。 因此，输入数据包含两个图像。 众所周知，我们无法仅使用两个图像来训练我们的深度神经网络，因为这将导致模型的过度拟合。 过度拟合模型基本上意味着我们的模型将在训练数据上给出最佳分数，而在测试或验证数据或之前从未见过的数据上则给出最佳分数。 因此，这种过度拟合的模型对于有效地训练我们的模型将毫无用处，我们将在这些输入图像的帮助下制作更多图像。 我们将通过图像增强来实现。

Fig.5.Undissolved tantalum fragment in the FeTaNbTiZrMo alloy.

图5 FeTaNbTiZrMo合金中未溶解的钽碎片

We can use the Image Data Generator class to achieve this. First, we will make the object of this class. After that we will provide some parameters that are basically the fluctuations or feature that we want to provide the image like luminous intensity, width shift range, height shift range, etc. and we can iterate over the directory where the images are kept in, by providing the path in the function. In this way, we can generate numerous data. In this project, we have generated approximately 3000 images for each image.

我们可以使用Image Data Generator类来实现这一点。 首先，我们将成为此类的对象。 之后，我们将提供一些基本参数，这些参数基本上是我们想要提供图像的波动或特征，例如发光强度，宽度偏移范围，高度偏移范围等。我们可以通过以下方式遍历保存图像的目录：在函数中提供路径。 这样，我们可以生成大量数据。 在此项目中，我们为每个图像生成了大约3000张图像。

We created two datasets for the training and testing purpose. Python programming was used for the development of the code required for constructing the Convolutional Neural Network architecture. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics.

我们为训练和测试目的创建了两个数据集。 Python编程用于开发构建卷积神经网络体系结构所需的代码。 卷积神经网络(ConvNet / CNN)是一种深度学习算法，可以吸收输入图像，为图像中的各个方面/对象分配重要性(可学习的权重和偏差)，并能够区分彼此。 与其他分类算法相比，ConvNet中所需的预处理要低得多。 在原始方法中，过滤器是手工设计的，经过足够的培训，ConvNets可以学习这些过滤器/特征。

Results and Discussions

结果和讨论

The augmented image of the microstructure is shown in Figure 6.

显微组织的放大图如图6所示。

Model is compiled with loss-Binary cross-entropy and metrics-accuracy and optimizer is adam. To prevent the model from Over-fitting, early stopping and model checkpoints are used so as to prevent a model from overtraining. Early Stopping is basically a process in which the model is stopped training when it doesn’t undergo any improvement. This parameter is provided in early stopping while making its object. This parameter is known as Patience. Metrics and mode are also provided as a parameter to test the model on the basis of that. Suppose metrics are value accuracy and mode is maximum, so when the model will not show any improvement (increment in value accuracy), it will wait till the patience parameter and after that, it will stop. The results were quite satisfactory when we trained our model against unlabelled images. As we can see in Figure 7, during prediction, almost every actual value is matched with predicted value so our model has been trained effectively.

使用损失-二进制交叉熵和度量准确性来编译模型，并且优化器是亚当。 为了防止模型过度拟合，使用了早期停止和模型检查点，以防止模型过度训练。 基本上，“早期停止”是一个过程，其中模型在未进行任何改进时就停止训练。 在使其成为对象的早期停止中提供此参数。 此参数称为耐心。 度量和模式也作为参数提供，以在此基础上测试模型。 假设度量标准是值准确性，并且模式是最大，那么当模型没有显示出任何改善(值准确性增加)时，它将等待直到耐心参数，然后才停止。 当我们针对未标记图像训练模型时，结果非常令人满意。 如图7所示，在预测期间，几乎每个实际值都与预测值匹配，因此我们的模型已得到有效训练。

The graphs in Figure 8 show the changes in metrics while training. As we can see, the model loss is getting lower as the epoch increases and accuracy is increasing as the epoch increases.

图8中的图形显示了训练期间指标的变化。 如我们所见，随着历时的增加，模型损失越来越小，随着历时的增加，模型的准确性也越来越高。

Conclusion

结论

It can be concluded that the current research is basically about image processing and classification, in which we first collected data due to a shortage of data, we did data augmentation to train our deep learning model, after that, we implemented our model architecture and compilation is done. After training, the results are shown. It is observed that the predicted value matches the actual value resulting in good accuracy for the image classification of the fragments present in HEAs.

可以得出结论，当前的研究基本上是关于图像处理和分类的，其中我们首先由于数据不足而收集数据，我们进行了数据扩充以训练我们的深度学习模型，之后，我们实现了模型架构和编译已经完成了。 训练后，将显示结果。 可以看出，预测值与实际值匹配，从而导致HEA中存在的碎片的图像分类具有良好的准确性。

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