Image Recognition Algorithm of Complex Cracks in Metro Tunnel based on Neural Network

Remote sensing includes all types of non-contact. surveys that are carried out from various measuring platforms. The tasks in this area are the following: inventory of agricultural land, control of the state of crops, forecasting yields. The aim of the work is to classify 6 types of crop images (wheat, rice, sugarcane, corn, cotton and jute) with greater accuracy. The paper considers an algorithm for primary processing and recognition of images of agricultural crops and algorithms for constructing a neural network for initial processing and recognition of images to solve problems of noise elimination, minimization, smoothing, normalization, segmentation and image recognition.

The paper is focused on the analysis of the relation between the stability of the flotation process and the efficiency of Machine Learning (ML) algorithms based on the flotation froth images. An ML process should enable researchers to construct Artificial Intelligence (AI) algorithms for flotation process control. The image of the flotation froth includes information characterizing the flotation process. The information can be extracted with the aid of the Image Recognition (IR) algorithms based on the ML. This enables construction of a flotation process control system in the mineral processing plant, which is based on the recognition of images of the flotation froth. The IR algorithms do not provide stable image recognition results and are not efficient in the situation where the parameters of the flotation process are highly unstable. The classification results were equal to 75.11% and 69.62% for a stable and unstable process, respectively. The experimental data collected at the Polish Pb/Zn mineral processing plant provided better insight to the relationships between the flotation process parameters and ML efficiency. These relationships were analyzed, and guidelines for the construction of the ML process for flotation process control have been formulated.

With the rapid development of urban rail transit and underground space development, the occurrence of excavation near existing tunnels is becoming more common. This excavation and unloading will inevitably cause soil deformation outside the pit and the tunnel buried therein. This paper presents an investigation into the entire excavation process and the deformation effects on the adjacent subway structure under complex construction environments, including soft soil foundations and adjacent subway intersections, based on measured data of pit construction. The horizontal and vertical displacements of deep soils in two pits were analyzed along with safety management measures to determine the horizontal displacement curve pattern and the lateral displacement development distribution law of deep soils. The analysis of subway deformation data shows that after excavation, the subway tunnel undergoes “lateral expansion” and “vertical compression” type deformation, accompanied by “in-pit” horizontal displacement and “settlement” vertical displacement. Furthermore, a correlation analysis reveals a clear linear relationship between pit deformation and subway structure deformation, which can be used as a prediction method for adjacent structure deformation. Finally, a comparative analysis of existing relevant influence zone models and tunnel deformation prediction formulas is carried out based on the monitoring. The results of this study have significant guiding implications for the excavation construction of soft soil foundations in the vicinity of subway crossings.

In portable precision visual measurement, processing result of the measurement image has an important influence on final measurement accuracy in large field working space. However, the images taken in large field working space always have the following characteristics: containing a large amount of information, having a high redundancy and including a lot of background noises. In order to solve these problems, precision image processing and recognition algorithm is researched. Firstly, portable precision visual measurement method is introduced. Then, the design method of global control points including noncoding points and coding points is displayed. Finally, the image processing and recognition algorithm is investigated in detail, including the largest variance threshold method used to partly binary the image, the detection algorithm of global control points based on the constraint of distance and acreage and coding value recognition method through correcting the image feature. A large amount of experiments show that the image processing and recognition algorithm is accurate and effective to processing the image taken in the working space.

In order to get a good image recognition model, a lot of high-quality data must be trained. However, some special fields (such as battlefield, rare medical images, etc.) have the characteristics of difficult to obtain data and difficult to label. In order to get a small batch image recognition model with good performance under the condition of insufficient data, a Synchronous Deep Reinforcement Learning algorithm for small-batch image recognition is proposed, and the algorithm is used to recognize image data such as combat elements. The algorithm is based on reinforcement learning and convolutional neural network and so on, it is obtained by optimizing it. Through experimental analysis, the algorithm can quickly realize small batch image classification, and the accuracy rate is higher than other similar algorithms, which proves the practicability and feasibility of this algorithm.

Traditional image recognition algorithms often face problems such as low recognition accuracy and insufficient robustness when facing complex scenes and multi-class image data. To this end, a hierarchical discriminant analysis (HDA)-based image recognition algorithm was proposed, which effectively improves image recognition performance by constructing a multi-scale feature extraction module, principal component analysis (PCA) dimensionality reduction, attention mechanism, and dynamic hierarchical adjustment strategy combined with a hierarchical feature extraction and discrimination model. The experiment was conducted on three public datasets: CIFAR-10, ImageNet subset (selecting 100 categories with a total of 150000 images, based on covering common object categories and moderate data volume for fair validation of algorithm performance), and MNIST. The performance was compared with models such as VGG16, ResNet50, SVM, KNN, Hierarchical CNN, EfficientNet, GoogLeNet, etc. The results indicated that the proposed method had higher recognition accuracy than other comparative algorithms on different datasets, with accuracies exceeding 90%. The proposed method performed better in terms of mean absolute error and root mean squared error. The F1 value curve of the proposed method was located at the top of the coordinate axis, reaching a maximum value of 92.39%, which was 14.56% higher than the lowest value of 78.24% in the EfficientNet model. This algorithm has better recognition accuracy than traditional algorithms on multiple public datasets, and has strong anti-interference ability and robustness, which can provide reference for optimizing the accuracy of image recognition.

The authors suggest a new technique for improvement of recognition reliability. The procedure is based on analysis of an image at several degrees of detail produced in the course of discrete wavelet transformation of the image. The results are presented by efficiency analysis of the algorithm for recognition of images by their distinctive points.

With the increase in urban rail transit construction, instances of tunnel disease are on the rise, and cracks have become the focus of tunnel maintenance and management. Therefore, it is essential to carry out crack detection in a timely and efficient manner to not only prolong the service life of the tunnel but also reduce the incidence of accidents. In this paper, the design and structure of a tunnel crack detection system are analyzed. On this basis, this paper proposes a new method for crack identification and feature detection using image processing technology. This method fully considers the characteristics of tunnel images and the combination of these characteristics with deep learning, while a deep convolutional network (Single-Shot MultiBox Detector (SSD)) is proposed based on deep learning for object detection in complex images. The experimental results show that the test set accuracy and training set accuracy of the support vector machine (SVM) in the classification comparison test are up to 88% and 87.8%, respectively; while the test accuracy of Alexnet’s deep convolutional neural network-based classification and identification is up to 96.7%, and the training set accuracy is up to 97.5%. It can be seen that this deep convolutional network recognition algorithm based on deep learning and image processing is better and more suitable for the detection of cracks in subway tunnels.

Architectures for the development of image recognition algorithms must support the implementation of systematic procedures for solving image recognition problems. All too often, designers develop image recognition architectures in an ad hoc fashion which lacks the structure to meet long term needs. Vendors typically supply customers with standard image processing libraries and display tools. Combining these tools and formulating development strategies have remained stumbling blocks in the design of complete image recognition algorithm development environments. In this paper, an architecture is presented which provides a well defined framework, and at the same time is sufficiently flexible to accommodate images of multiple sensor and data types. The primary components of the architecture are: ground-truthing, preprocessing (which includes image processing and segmentation), feature extraction, classification, and performance analysis. Powerful and well defined data structures are exploited for each of the primary components. Groups of programs called tasks manipulate one or more of these data structures, each task belonging to one of the primary components. Multiple tasks can be executed in an unsupervised mode over an entire database of images. Results are then subjected to performance analysis and feedback. A description of the primary components and how they are integrated to facilitate the rapid prototyping and development of image recognition algorithms is presented.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An algorithm for image recognition and retrieval of image from image collection is developed. Basis of the algorithm is the progressive wavelet correlation. The recognition consists of three incremental steps, each of them quadruples the number of correlation points. The process can be aborted at any stage if the intermediate results indicate that the correlation will not result in a match. The final result is the recognition and retrieval of the required image, if exists in the image collection. Instructions for the choice of correlation threshold value for obtaining desired results are defined. We perform a series of image search experiments that cover the following scenarios: the given image is present in the database; copies of the given image are present but with different names; similar (but not identical) images are present; and the given image is not present. Experiments are performed with data bases up to 1000 images, using the Oracle database and the Matlab component Database Toolbox for operations with databases.

This paper describes a two-stage recognition method that reduces the calculation load of correlation and improves recognition accuracy in statistical image recognition. It consists of an image screening and recognition stage. Image screening selects a candidate set of subimages that are similar to the object class using a lower dimensional feature vector. Since recognition is made for the selected subimages set using a higher dimensional feature vector, overall recognition efficiency is improved. The classifier in recognition designed from the selected subimages also improves recognition accuracy because selected subimages are less contaminated than the original ones. A screening criterion for measuring overall efficiency and accuracy of recognition is introduced to be exploited in designing the feature spaces of image screening and recognition. The results of experiments for the eye- and mouth-area detection in face images and text-area detection in document images show that the designed feature spaces improve recognition accuracy and more efficiency than does the conventional one-stage recognition method.

Scene texts refer to arbitrary text found in images captured by cameras in real-world settings. The tasks of text detection and recognition are critical components of computer vision, with applications spanning scene understanding, information retrieval, robotics, and autonomous driving. Despite significant advancements in deep learning methods, achieving accurate text detection and recognition in complex images remains a formidable challenge for robust real-world applications. Several factors contribute to these challenges. First, the diversity of text shapes, fonts, colors, and styles complicates detection efforts. Second, the myriad combinations of characters, often with unstable attributes, make complete detection difficult, especially when background interruptions obscure character strokes and shapes. Finally, effective coordination of multiple sub-tasks in end-to-end learning is essential for success. This research aimed to tackle these challenges by enhancing text discriminative representation. This study focused on two interconnected problems: Scene Text Recognition (STR), which involves recognizing text from scene images, and Scene Text Detection (STD), which entails simultaneously detecting and recognizing multiple texts within those images. This research focuses on implementing and evaluating the Efficient and Accurate Scene Text Detector (EAST) algorithm for text detection and recognition in natural scene images. The study aims to compare the performance of three prominent Optical Character Recognition (OCR) techniques—TesseractOCR, PaddleOCR, and EasyOCR. The EAST model was applied to a series of sample test images, and the results were visually represented with bounding boxes highlighting the detected text regions. The inference times for each image were recorded, highlighting the algorithm's efficiency, with average times of 0.446, 0.439, and 0.440 seconds for the respective test images. These results indicate that the EAST algorithm is accurate and operates in real-time, making it suitable for applications requiring immediate text recognition.

Deep Learning algorithms are designed in such a way that they mimic the function of the human cerebral cortex. These algorithms are representations of deep neural networks i.e. neural networks with many hidden layers. Convolutional neural networks are deep learning algorithms that can train large datasets with millions of parameters, in form of 2D images as input and convolve it with filters to produce the desired outputs. In this article, CNN models are built to evaluate its performance on image recognition and detection datasets. The algorithm is implemented on MNIST and CIFAR-10 dataset and its performance are evaluated. The accuracy of models on MNIST is 99.6 %, CIFAR-10 is using real-time data augmentation and dropout on CPU unit.

Nowadays, the information processing capabilities and resource storage capabilities of computers have been greatly improved, which also provides support for the neural network technology. Convolutional neural networks have good characterization capabilities in computer vision tasks, such as image recognition technology. Aiming at the problem of high similarity image recognition and classification in a specific field, this paper proposes a high similarity image recognition and classification algorithm fused with convolutional neural networks. First, we extract the image texture features, train different types, and different resolution image sets and determine the optimal texture different parameter values. Second, we decompose the image into subimages according to the texture difference, extract the energy features of each subimage, and perform classification. Then, the input image feature vector is converted into a one-dimensional vector through the alternating 5-layer convolution and 3-layer pooling of convolutional neural networks. On this basis, different sizes of convolution kernels are used to extract different convolutions of the image features, and then use convolution to achieve the feature fusion of different dimensional convolutions. Finally, through the increase in the number of training and the increase in the amount of data, the network parameters are continuously optimized to improve the classification accuracy in the training set and in the test set. The actual accuracy of the weights is verified, and the convolutional neural network model with the highest classification accuracy is obtained. In the experiment, two image data sets of gems and apples are selected as the experimental data to classify and identify gems and determine the origin of apples. The experimental results show that the average identification accuracy of the algorithm is more than 90%.

Hand recognition using OpenCV is a burgeoning field with several applications, including virtual environment control, sign language translation, robot remote control, and musical composition. One such application is gesture recognition, which allows users to practically create anything on the screen by simply using their hands. Another exciting function that is prevalent is searching by image. The concept behind searching by image is to take a picture as an input and generate metadata from it that may be used later. In this paper, integration of both the aforementioned methodologies is done to produce an application in which a user may draw any image using numerous hand motions that are captured by the user's webcam. This image is then processed by an image recognition algorithm, which generates metadata that may be used to search for the entity depicted in the image.