Research on Image Recognition Based on Convolutional Neural Networks

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .

HighlightsWhat are the main findings?Proposed an innovative two-channel hybrid model by integrating convolutional neural networks (CNNs) with a text-based classifier.Leveraged an extended dataset derived from multiple object recognition models, increasing input data diversity and achieving a text-based classifier accuracy of 73.30%. Achieved a significant improvement of 8.33% in accuracy compared to CNN-only models, with the hybrid model attaining an accuracy of 90.46%.What is the implication of the main finding?Efficient and Scalable Methodology: Utilized EfficientNet for CNN-based feature extraction and Bag-of-Words for text representation, ensuring computational efficiency and scalability.Application Potential: Addressed challenges in indoor scene recognition, such as complex backgrounds and object diversity, demonstrating significant potential for applications in robotics, intelligent surveillance, and assistive systems.Indoor scene recognition is a computer vision task that identifies various indoor environments, such as offices, libraries, kitchens, and restaurants. This research area is particularly significant for applications in robotics, security, and assistance for individuals with disabilities, as it enables the categorization of spaces and the provision of contextual information. Convolutional Neural Networks (CNNs) are commonly employed in this field. While CNNs perform well in outdoor scene recognition by focusing on global features such as mountains and skies, they often struggle with indoor scenes, where local features like furniture and objects are more critical. In this study, the “MIT 67 Indoor Scene” dataset is used to extract and combine features from both a CNN and a text-based model utilizing object recognition outputs, resulting in a two-channel hybrid model. The experimental results demonstrate that this hybrid approach, which integrates natural language processing and image processing techniques, improves the test accuracy of the image processing model by 8.3%, achieving a notable success rate. Furthermore, this study offers contributions to new application areas in remote sensing, particularly in indoor scene understanding and indoor mapping.

In recent years, convolutional neural networks have been widely used in image processing and have shown good results. Particularly useful was their ability to automatically extract image features (textures and shapes of objects). The article proposes a method that improves the accuracy and speed of recognition of an ultra-precise neural network based on image recognition of people’s faces. At first, a recurrent neural network is introduced into the convolutional neural network, thereby studying the characteristics of the image more deeply. Deep image characteristics are studied in parallel using a convolutional and recurrent neural network. In line with the idea of skipping the ResNet convolution layer, a new ShortCut3- ResNet residual module is built. A double optimization model is created to fully optimize the convolution process. A study of the influence of various parameters of a convolutional neural network on network performance is demonstrated, also analyzed using simulation experiments. As a result, the optimal parameters of the convolutional neural network are established. Ex- periments show that the method presented in this paper can study various images of people’s faces regardless of age, gender, and also improves the accuracy of feature extraction and image recognition ability.

To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.

The electrocardiogram (ECG) is a non-invasive procedure used to diagnose cardiovascular disorders. The 2D ECG system gives important information on the electrophysiology of heart illnesses and possible brain changes, as well as important information about the cardiovascular system’s functional characteristics. The classification proposed system detected four different classes from the ECG cases: myocardial infarction (MI), Arrhythmia, History MI, and Normal case. The suggested system used three steps for classification ECG, include: Image processing, feature extraction, and classification. The first step involves utilizing Image preprocessing methods to prepare and enhance the input images, which includes two steps: converting RGB images to a binary image and applying filters to reduce noise from a binary image. Utilizing local binary pattern algorithm, features are retrieved from the images in the second step (LBP). The collected characteristics are then supplied into a 1D "convolutional neural network" (1D-CNN) model for object detection and classification in the third step. The findings demonstrate that the suggested model of the CNN has a high accuracy rate, with a classification accuracy rate of 97 percent. The suggested method works extremely well in the categorization of the 2D ECG, as demonstrated by the low rate of false alarms and high rate of accuracy.

In this paper, we look at how to design and build a system to find tumors using 2 Convolutional Neural Network (CNN) models. With the help of digital image processing and deep Learning, we can make a system that automatically diagnoses and finds different diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data enhancement, feature extraction, and classification. These options are set up so that the CNN model can give the best results. During the training phase, the learning rate is used to change the weights and bias. The learning rate also changes the weights. One Epoch is when all of the training images are shown to the model. As the training data may be very large, the data in each epoch are split into batches. Every epoch has a training session and a test session. After each epoch, the weights are changed based on how fast the CNN is learning. This is done with the help of optimization algorithms. The suggested technique uses the anticipated mean intersection over union value to identify failure instances in addition to forecasting the mean intersection over union. This paper talks about how to separate brain tumors from magnetic resonance images of patients taken from "Brain web." Using basic ideas of digital image processing, magnetic resonance images are used to extract and find tumors using a hybrid method. In this paper, the proposed algorithm is applied with the help of MATLAB. In medical image processing, brain tumor segmentation is an important task. The goal of this paper is to look at different ways to divide brain tumors using magnetic resonance imaging. Recently, automatic segmentation using deep learning methods has become popular because these methods get the best results and are better at solving this problem than others. Deep learning methods can also be used to process and evaluate large amounts of magnetic resonance imaging image data quickly and objectively. A classification method based on a convolution neural network is also added to the proposed scheme to make it more accurate and cut down on the amount of time it takes to do the calculations. Also, the results of the classification are given as images of a tumor or a healthy brain. The training is 98.5% correct. In the same way, both the validation accuracy and validation loss are high.

High-level feature extraction and hierarchical feature representation of image objects with a convolutional neural network (CNN) can overcome the limitations of the traditional building detection models using middle/low-level features extracted from a complex background. Aiming at the drawbacks of manual village location, high cost, and the limited accuracy of building detection in the existing rural building detection models, a two-stage CNN model is proposed in this letter to detect rural buildings in high-resolution imagery. Simulating the hierarchical processing mechanism of human vision, the proposed model is constructed with two CNNs, whose architectures can automatically locate villages and efficiently detect buildings, respectively. This two-stage CNN model effectively reduces the complexity of the background and improves the efficiency of rural building detection. The experiments showed that the proposed model could automatically locate all the villages in the two study areas, achieving a building detection accuracy of 88%. Compared with the existing models, the proposed model was proved to be effective in detecting buildings in rural areas with a complex background.

— In today's era, the challenge of object detection within laboratory spaces is paramount. The need for automated monitoring systems supporting object detection within laboratory settings, particularly for service robots, is crucial for scientific research and safety. Utilizing convolutional artificial neural networks has shown promising capabilities in object recognition. This paper proposes a novel method for detecting and localizing specific objects within laboratory environments using deep learning techniques. Employing a lightweight primary feature extractor allows for the identification of crucial object features without imposing excessive computational demands. Additionally, a depthwise separable convolution module is introduced, designed to capture essential components within multi-level convolutional patches specific to laboratory conditions. To address the challenges encountered within laboratory spaces, a new dataset is proposed, encompassing issues such as lighting variations, blurriness, occlusions, and complex backgrounds. Evaluation results indicate that the proposed model surpasses other lightweight object detection models in laboratory settings, offering high accuracy and efficiency. This model can be efficiently implemented on low-tier devices, ensuring real-time object detection and processing within laboratory environments. Keywords — object detection, service robot, convolutional neural networks, You Only Look Once version 5. Abstrak — Di era sekarang ini, tantangan deteksi objek di dalam ruang laboratorium menjadi sangat penting. Kebutuhan akan sistem pemantauan otomatis yang mendukung deteksi objek di dalam laboratorium, khususnya untuk robot servis, sangat penting untuk penelitian ilmiah dan keselamatan. Memanfaatkan jaringan saraf tiruan convolutional telah menunjukkan kemampuan yang menjanjikan dalam pengenalan objek. Makalah ini mengusulkan metode baru untuk mendeteksi dan melokalisasi objek tertentu di dalam lingkungan laboratorium menggunakan teknik pembelajaran mendalam. Menggunakan ekstraktor fitur utama yang ringan memungkinkan identifikasi fitur objek yang penting tanpa membebani komputasi yang berlebihan. Selain itu, modul konvolusi yang dapat dipisahkan secara mendalam juga diperkenalkan, yang dirancang untuk menangkap komponen penting dalam patch konvolusi multi-level yang spesifik untuk kondisi laboratorium. Untuk mengatasi tantangan yang dihadapi di dalam ruang laboratorium, dataset baru diusulkan, yang mencakup masalah seperti variasi pencahayaan, kekaburan, oklusi, dan latar belakang yang kompleks. Hasil evaluasi menunjukkan bahwa model yang diusulkan melampaui model pendeteksian objek ringan lainnya di lingkungan laboratorium, menawarkan akurasi dan efisiensi yang tinggi. Model ini dapat diimplementasikan secara efisien pada perangkat tingkat rendah, memastikan pendeteksian dan pemrosesan objek secara real-time di lingkungan laboratorium. Kata kunci — deteksi objek, robot layanan, jaringan saraf konvolusi, Anda Hanya Melihat Sekali versi 5.

Plant leaf diseases pose significant challenges to agricultural productivity, necessitating early detection and intervention strategies. Recent research has increasingly turned to artificial intelligence (AI) techniques, particularly image processing and deep learning, to address these challenges. The paper provides a technical view of the application of Artificial Intelligence in plant leaf disease detection. Key aspects covered include datasets, image processing methodologies, deep learning algorithms, and evaluation metrics commonly utilized in this domain. An emphasis is placed on the important role of annotated datasets in the training of AI models, facilitating the development of accurate classification systems. Image processing techniques, encompassing tasks such as enhancement, segmentation, and feature extraction, are discussed for their role in preprocessing leaf images to extract salient features. Deep learning algorithms, notably convolutional neural networks (CNNs), are highlighted for their effectiveness in automatically learning discriminative features from input images and achieving high accuracy rates in disease classification tasks. The paper illuminates the significance of employing evaluation metrics such as accuracy, precision, recall, and F1 score as quantitative benchmarks to gauge the efficacy of model performance. It highlights the triumphs observed across diverse studies harnessing AI methodologies for the detection of plant leaf diseases, emphasizing their transformative capacity in reshaping agricultural management paradigms. Future research directions are outlined, including the exploration of transfer learning methods to capitalize on pre-trained models and the integration of additional sensing technologies like hyperspectral imaging and drones for enhanced disease detection capabilities. These advancements are poised to further refine the accuracy and efficiency of plant leaf disease detection systems, thereby offering substantial benefits to agriculture and food production sectors.

Different kinds of techniques are evaluated and analyzed for various classification models for the detection of diseases of citrus fruits. This paper aims to systematically review the papers that focus on the prediction, detection, and classification of citrus fruit diseases that have employed machine learning, deep learning, and statistical techniques. Additionally, this paper explores the present state of the art of the concept of image acquisition, digital image processing, feature extraction, and classification approaches, and each one is discussed separately. A total of 78 papers are selected after applying primary selection criteria, inclusion/exclusion criteria, and quality assessment criteria. We observe that the following are widely used in the selected studies: hyperspectral imaging systems for the image acquisition process, thresholding for image processing, support vector machine (SVM) models as machine learning (ML) models, convolutional neural network (CNN) architectures as deep learning models, principal component analysis (PCA) as a statistical model, and classification accuracy as evaluation parameters. Moreover, the color feature is the most popularly used feature for the RGB color space. From the review studies that performed comparative analyses, we find that the best techniques that outperformed other techniques in their respective categories are as follows: SVM among the ML methods, ANN among the neural network networks, CNN among the deep learning methods, and linear discriminant analysis (LDA) among the statistical techniques.This study concludes with meta-analysis, limitations, and future research directions.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

Content-Based Image retrieval (CBIR) has gained a magnificent deal of consideration because of the digital image data's epidemic growth. The advancement of deep learning has enabled Convolutional Neural Networks to become an influential technique for extraction of discriminative image features. In recent years, convolutional neural networks (CNNs) have proven extremely effective at extracting unique information from images. In contrast to text-based image retrieval, CBIR gathers comparable images based primarily on their visual content. The use of deep learning, especially CNNs, for feature extraction and image processing has been shown to perform better than other techniques. In the proposed study, we investigate CNNs for CBIR focusing on how well they extract discriminative visual features and facilitate accurate image retrieval. Also Principal Component Analysis and Linear Discriminant Analysis are combined for optimization of features resulting in boosting the retrieval results. Using hierarchical representations learned by CNNs, we aim to improve retrieval accuracy and efficiency. In comparison with conventional retrieval techniques, our proposed CBIR system shows superior performance on a benchmark dataset.

Tire defects are crucial for safe driving. Specialized experts or expensive tools such as stereo depth cameras and depth gages are usually used to investigate these defects. In image processing, feature extraction, reduction, and classification are presented as three challenging and symmetric ways to affect the performance of machine learning models. This paper proposes a hybrid system for cracked tire detection based on the adaptive selection of correlation features and deep belief neural networks. The proposed system has three steps: feature extraction, selection, and classification. First, the oriented gradient histogram extracts features from the tire images. Second, the proposed adaptive correlation feature selection selects important features with a threshold value adapted to the nature of the images. The last step of the system is to predict the image category based on the deep belief neural networks technique. The proposed model is tested and evaluated using real images of cracked and normal tires. The experimental results show that the proposed solution performs better than the current studies in effectively classifying tire defect images. The proposed hybrid cracked tire detection system based on adaptive correlation feature selection and Deep Belief Neural Networks’ performance provided better classification accuracy (88.90%) than that of Belief Neural Networks (81.6%) and Convolution Neural Networks (85.59%).

The classification of mango fruit ripeness levels is currently predominantly done manually, which unfortunately has several drawbacks. One of the primary shortcomings is the lack of consistency in accuracy, often resulting in differences among operators conducting the sorting. On the other hand, in image classification processes, a combination of the Discrete Cosine Transform (DCT) and Convolutional Neural Network (CNN) methods is utilized. DCT is a technique commonly used in image processing, especially for image pictures. In this research, there is a proposal to merge the Discrete Wavelet Transform method with the Convolutional Neural Network (CNN). Currently, CNN is one of the methods that provides the most significant results in image recognition. CNN attempts to mimic the image recognition system in the human brain, particularly the visual cortex, allowing it to efficiently process image information. The DCT method is used to transform image data into a frequency image form, which is subsequently employed in feature extraction in the Deep Neural Networks classification method. The research results indicate that the combined method of Discrete Cosine Transform and Convolutional Neural Network achieves the highest accuracy rate of 93.33% in classifying mango fruit ripeness levels. This outcome demonstrates significant potential for automating the mango ripeness classification process with high accuracy, overcoming the inconsistencies associated with manual approaches.

Among the list of dreadful diseases, 'Brain Tumor and Brain Cancer' is one of them. According to survival rate, after the detection of cancerous Brain or Central Nervous System (CNS) tumor, 34% of men and 36% of women survive at least 5 years. Therefore, 'Early Detection of tumor' can be a savior for so many lives and artificial intelligence will make the way very accurate and appropriate. A new non-invasive approach to detect brain tumors in early-stage, cost-efficiently with the lowest risk factor is-'Liquid Biopsy'. It is a technique for analyzing non-solid biological tissue, i.e. - blood, plasma, cerebrospinal fluid (CSF). As human inspection maybe error full and inaccurate, in this paper, we have used 'k-means clustering' in 'Image Processing' as visual perception of artificial intelligence for detection of 'CTC' and we have used the technique 'Convolutional Neural Network' (CNN) for ctDNA detection and 'Support Vector Machine'(SVM) algorithm for classification of ctDNA and normal cell-free DNA (cfDNA). 'ctDNA' could provide clues for growth factor of cancerous cells, type of tumors, location, etc. Therefore, our work provides a new way to early detection and a new prospect for early cure.