Development of GUI-based deep learning and image processing system for legume nodule segmentation and classification

Vision-based structural displacement measurement under ambient-light changes via deep learning and digital image processing

During the past decade, deep learning is one of the essential breakthroughs made in artificial intelligence. In particular, it has achieved great success in image processing. Correspondingly, various applications related to image processing are also promoting the rapid development of deep learning in all aspects of network structure, layer designing, and training tricks. However, the deeper structure makes the back-propagation algorithm more difficult. At the same time, the scale of training images without labels is also rapidly increasing, and class imbalance severely affects the performance of deep learning, these urgently require more novelty deep models and new parallel computing system to more effectively interpret the content of the image and form a suitable analysis mechanism. In this context, this survey provides four deep learning model series, which includes CNN series, GAN series, ELM-RVFL series, and other series, for comprehensive understanding towards the analytical techniques of image processing field, clarify the most important advancements and shed some light on future studies. By further studying the relationship between deep learning and image processing tasks, which can not only help us understand the reasons for the success of deep learning but also inspires new deep models and training methods. More importantly, this survey aims to improve or arouse other researchers to catch a glimpse of the state-of-the-art deep learning methods in the field of image processing and facilitate the applications of these deep learning technologies in their research tasks. Besides, we discuss the open issues and the promising directions of future research in image processing using the new generation of deep learning.

The Unmanned Aerial Vehicle (UAV) has been around for a long time but has been widely used recently by humans. Their acceptance of various communications-based applications is expected to improve coverage, compared to traditional ground-based solutions. In this paper, the Deep-learning and Image Processing Process framework is expected to provide solutions to the various problems already identified when UAVs are used for communication purposes. UAVs are used in disaster relief because of their accessibility even in inaccessible places. In this paper, we propose research into Deep learning and Image Processing strategies for UAVs. In deep learning is a form of machine learning that teaches computers to do what comes naturally to people: learn by example and get a lot of attention recently and for a good reason. It achieves previously impossible results. Image processing is the process of performing a specific task on an image, finding an enhanced image or extracting useful information from it. So our paper has the idea of using in depth face recognition and photo processing a digital photo taken by the UAV to identify victims of rescue, overcoming back to the latest UAV technology some of which include blurry images, unable to identify the victim when there are too many objects and much more. The solution includes a variety of features that allow for the distribution of images. It includes features and presentation of image detection and demonstrates the effectiveness of drone use in damage applications.

—Brain tumor detection is a critical aspect of neurooncology, where timely and accurate diagnosis significantly impacts patient outcomes. Traditional imaging techniques, while useful, are often limited by variations in image quality, illumination inconsistencies, and the complex nature of brain tumors themselves, which vary widely in size, shape, and texture. This paper presents a deep learning- based approach to brain tumor detection using Convolutional Neural Networks (CNNs) combined with advanced image processing methods. Our methodology involves preprocessing brain MRI scans using histogram equalization, morphological operations, and data augmentation to address illumination issues and enhance the tumor region. Following preprocessing, the images are fed into a CNN, where transfer learning from the Sequential model is applied to improve classification accuracy. Experimental results demonstrate high recall and precision rates, affirming the model’s robustness in detecting brain tumors in MRI images. This approach not only addresses the challenge of inconsistent MRI scan quality but also reduces the risk of misclassification, showing promise for clinical application. Keywords— Brain Tumor Detection, Computer-aided Diagnosis, Computer Vision, Convolutional Neural Networks, Deep Learning, Image Processing, Transfer Learning.

To improve the safety of road tunnel pavement, the research established road tunnel pavement water seepage recognition models based on deep learning technology, and a water seepage area extraction model based on image processing technology to finally achieve accurate detection of water seepage on tunnel pavements. First, the deep learning models EfficientNet water seepage recognition model and MobileNet water seepage recognition model were built, the models were trained with the self-collected pavement seepage data set, and the F1 score was introduced to evaluate the accuracy and comprehensive performance of the two models in predicting different categories of water seepage characteristics. Then three grayscale processing methods, the cvtColor function, mean method and maximum method, six global threshold segmentation methods, Otsu thresholding method, THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO and THRESH_TOZERO_INV, three filtering methods, namely Gaussian filtering, median filtering and morphological open operation, as well as small connected domain removal, were used to reduce the noise of the images. Finally, the seepage area image calculation method was proposed based on the processed images to predict the actual pavement seepage area. The results show that the recognition accuracy of the EfficientNet water seepage recognition model is 99.85% and 97.53% in the training and validation sets respectively, which is 2.85% and 0.76% higher than the 97% and 96.77% of the MobileNet model. The average F1 score of the EfficientNet model is 95.22%, which is 5.05% higher than that of the MobileNet model, for the four types of seepage feature images: point seepage, line seepage, surface seepage and no seepage. The cvtColor function for grayscale processing, THRESH_BINARY for threshold segmentation and a combination of median filtering and morphological open operation for image noise reduction can effectively extract the seepage features. The area calculation is performed by the seepage area image calculation method, and the average error between the predicted value and the actual seepage area is 8.30%, which can better achieve the accurate extraction of the seepage area.

This manuscript proposes an automatic reading detection system for an analogue gauge using a combination of deep learning, machine learning, and image processing. The study suggests image-processing techniques in manual analogue gauge reading that include generating readings for the image to provide supervised data to address difficulties in unsupervised data in gauges and to achieve better accuracy using DenseNet 169 compared to other approaches. The model uses artificial intelligence to automate reading detection using deep transfer learning models like DenseNet 169, InceptionNet V3, and VGG19. The models were trained using 1011 labeled pictures, 9 classes, and readings from 0 to 8. The VGG19 model exhibits a high training precision of 97.00% but a comparatively lower testing precision of 75.00%, indicating the possibility of overfitting. On the other hand, InceptionNet V3 demonstrates consistent precision across both datasets, but DenseNet 169 surpasses other models in terms of precision and generalization capabilities.

we will conduct corrosion detection based on deep learning using images of monorail bridges taken from an onboard camera, and then investigate whether it is possible to evaluate the progress of corrosion using the additive color mixing method, using images taken before and after the progress of corrosion.

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.

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections.

The edible potato comes in fifth for human consumption and fourth among main food crops. Since it is a crop that is vegetative grown, many pests and disease can be passed along from one generation to the next. Crop diseases, which have a negative impact on food security as well as economic losses, have a significant impact on the production and quality of yields from potato crops. Thus, the application of unique and precise deep learning-based algorithms for disease detection and classification is highly required. Identifying weaknesses in agricultural products, particularly potatoes requires the use of machine vision and image processing techniques. Deep learning and image processing have been used in agriculture to classify and number of disease and pests affecting potatoes has grown, and study in this area is still continuing. The use of artificial intelligence and image processing in agriculture for the classification and identification of potato pests and disease has grown, and work in this area is still ongoing. Different deep learning techniques, such as VGG19, VGG16, Google Net, Alex Net, and convolution neural network methods, can be used to address the disease problem in potatoes. These methods also examined multiple classes of potato diseases as: Healthy, Black Leg, Black Scurf, Pink Rot, Common Scab, etc. Food safety could be seriously threatened by the spread of potato disease. In this article, deep learning techniques for early detection of potato disease are discussed.

PurposeOwing to the increased mortality of heart diseases worldwide, especially myocardial infarction (MI), early detection is essential for improved diagnosis and treatment. The main purpose of this study is to develop a myocardial infarction detection method that combines deep learning and image processing, focusing on abnormalities in left ventricular (LV) wall motion.MethodsThe proposed method primarily uses the LV wall motion movement as a feature to train an LSTM network for MI detection. LV wall motion annotated by expert cardiologists was used as the ground truth. Accuracy, sensitivity, specificity, and area under the curve (AUC) were used to evaluate model performance. The proposed method primarily uses LV wall motion as a feature, combined with LV size and image pixels, to improve diagnostic accuracy over existing computer-aided design (CAD) systems.ResultsThe LSTM model achieved the highest diagnostic performance when trained on a combination of LV wall motion, LV size, and image pixel features with an accuracy of 95%, sensitivity of 96%, specificity of 94%, and an AUC value of 0.98. The LSTM model significantly outperformed models trained on individual feature sets or conventional machine learning algorithms. The inclusion of LV wall motion analysis improved accuracy by 10% compared to using only LV size and pixel data.ConclusionOur MI diagnosis system uses echocardiographic image analysis and LSTM-based deep learning to accurately detect LV wall motion issues related to MI. Compared with current CAD systems, the inclusion of LV wall motion analysis significantly improves diagnosis accuracy. The proposed system can help physicians detect MI early, thereby accelerating treatment and improving patient outcomes.

Our innovative approach completely revamps the process of verifying bank checks by leveraging deep learning and image processing. To make the cheque truncation mechanism far more efficient, we reduce the amount of human interaction. Our approach efficiently retrieves important details from the check booklet, including the bank branch code, check number, precise amount, account number, and unique signature patterns. We utilized the IDRBT cheque dataset and convolutional neural networks based on deep learning for our study. This enhanced assessment of handwritten components on bank cheques achieved 99.14 percent accuracy in identifying handwritten numeric characters. We achieved an impressive 97.7 percent accuracy while utilizing MATLAB's integrated optical character recognition (OCR) technique to interpret machine-printed text. Utilizing Scale Invariant Feature Transform (SIFT) for feature extraction and Support Vector Machine (SVM) for classification resulted in a notable 98.10 percent accuracy in signature verification.

Decoding speech from labial movements is an emerging area of research that bridges computer vision, linguistics, and artificial intelligence. By analysing the dynamic patterns of lip shapes, positions, and motions, systems can infer spoken words even in the absence of acoustic signals. This technique is particularly valuable in noisy environments, for individuals with hearing impairments, and in applications requiring silent communication. Recent advances in deep learning and image processing have enabled more accurate mapping between visual features and phonetic units, improving recognition rates and robustness across diverse speakers. The integration of multimodal cues, such as facial expressions and contextual language models, further enhances performance, making visual speech decoding a promising complement to traditional audio-based systems. Ultimately, this field contributes to more inclusive human-computer interaction and opens new possibilities for assistive technologies and secure communication.

General matrix multiplication (GEMM) plays a paramount role in a broad range of domains such as deep learning, scientific computing, and image processing. The primary optimization method is to partition the matrix into many tiles and exploit the parallelism within and between tiles. The tiling hierarchy closely mirrors the thread hierarchy on GPUs. In practice, GPUs can fully unleash its computing power only when the matrix size is large and there are sufficient number of tiles and workload for each tile. However, in many real-world applications especially deep learning domain, the matrix size is small. To this end, prior work proposes batched GEMM to process a group of small independent GEMMs together by designing a single CUDA kernel for all of these GEMMs.