Image Recognition Model Research Articles

DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention

Blurry scenarios, such as light reflections and water ripples, often affect the clarity and signal-to-noise ratio of fish images, posing significant challenges for traditional deep learning models in accurately recognizing fish species. Firstly, deep learning models rely on a large amount of labeled data. However, it is often difficult to label data in blurry scenarios. Secondly, existing deep learning models need to be more effective for the processing of bad, blurry, and otherwise inadequate images, which is an essential reason for their low recognition rate. A method based on the diffusion model and attention mechanism for fish image recognition in blurry scenarios, DiffusionFR, is proposed to solve these problems and improve the performance of species recognition of fish images in blurry scenarios. This paper presents the selection and application of this correcting technique. In the method, DiffusionFR, a two-stage diffusion network model, TSD, is designed to deblur bad, blurry, and otherwise inadequate fish scene pictures to restore clarity, and a learnable attention module, LAM, is intended to improve the accuracy of fish recognition. In addition, a new dataset of fish images in blurry scenarios, BlurryFish, was constructed and used to validate the effectiveness of DiffusionFR, combining bad, blurry, and otherwise inadequate images from the publicly available dataset Fish4Knowledge. The experimental results demonstrate that DiffusionFR achieves outstanding performance on various datasets. On the original dataset, DiffusionFR achieved the highest training accuracy of 97.55%, as well as a Top-1 accuracy test score of 92.02% and a Top-5 accuracy test score of 95.17%. Furthermore, on nine datasets with light reflection noise, the mean values of training accuracy reached a peak at 96.50%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 90.96% and 94.12%, respectively. Similarly, on three datasets with water ripple noise, the mean values of training accuracy reached a peak at 95.00%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 89.54% and 92.73%, respectively. These results demonstrate that the method showcases superior accuracy and enhanced robustness in handling original datasets and datasets with light reflection and water ripple noise.

Image Recognition for Fruit-Picking Robots

This summary presents the solutions to the five questions posed in the competition regarding apple image recognition. The objective is to establish a robust apple image recognition model that accurately counts the number of apples, estimates their positions, determines their maturity state, calculates their masses, and recognizes them based on provided image datasets. The specific answers to each question are as follows: Using the image dataset of harvest-ready apples provided in Attachment 1, an image feature extraction method is employed to establish a mathematical model for counting the number of apples in each image. The distribution of all apples in Attachment 1 is then depicted in a histogram. The precise number of apples in each image is determined, enabling a comprehensive understanding of the dataset. Utilizing the image dataset of harvest-ready apples provided in Attachment 1, the positions of the apples in each image are identified. The left bottom corner of the image serves as the coordinate origin, and a two-dimensional scatter diagram is created, illustrating the geometric coordinates of all apples in Attachment 1. This analysis facilitates the visualization of the spatial distribution of apples within the dataset. Based on the image dataset of harvest-ready apples provided in Attachment 1, a mathematical model is developed to assess the maturity state of apples in each image. By applying this model, a histogram is generated, illustrating the distribution of maturity levels across all apples in Attachment 1. This analysis provides insights into the overall maturity status of the dataset.

Online Multimodal Compression using Pruning and Knowledge Distillation for Iris Recognition

Deep learning models have advanced to the forefront of image recognition tasks, resulting in high-performing but enormous neural networks with millions to billions of parameters. Yet, deploying these models in production systems imposes considerable memory limits. Hence, the research community is increasingly aware of the need for compression strategies that can reduce the number of model parameters and their resource requirement. Current compression techniques for deep learning models have limitations in efficiency and effectiveness, indicating that more research is required to develop more efficient and practical techniques capable of balancing the trade-offs between compression rate, computational cost, and accuracy. This study proposed a multimodal method by combining multimodal Pruning and Knowledge Distillation techniques for compressing the iris recognition model, which is the size constraint for many image recognition models. To maintain accuracy while shrinking the model’s size, the models are trained, compressed, and further retrained in the downstream job. The analysis includes both fully connected and convolutional layers. Experimentally, the findings show that the proposed technique can achieve 91% accuracy, the same as the existing or original model. Besides that, the model compression can reduce the size of the model almost six times, from 529MB to 90MB, which is a significantly reduced rate. The primary outcome of this study is developing a CNN lightweight model for iris recognition technology that can be used on mobile devices and is resource constrained.

Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images

This study explored the use of TensorFlow image recognition model to identify herbaceous mimosa (Mimosa strigillosa) from digital images. There is a demand for such technology toward digital mapping of the spatial distribution of these important perennial legumes in the context of pasture management and as well as management of reclamation ground cover landscapes. This study provided evidence of successful application of TensorFlow model for identification of herbaceous mimosa from digital images with final accuracy of 95 % or more. The complexity of ground images of multiple objects in this study is suspected to induce fluctuations in validation accuracy. Such fluctuation of the validation accuracy, however, was shown to decline over time as the accuracy increased with more processing epochs involved. Despite the downside of intensive data preparation and heavy computing resources, the approach tested in this study is promising toward the next step of the technology application for identification of herbaceous mimosa patches from images acquired using Unmanned Aerial Vehicle (UAV).

Aggregation-Based Perceptual Deep Model for Scenic Image Recognition

Aggregation-Based Perceptual Deep Model for Scenic Image Recognition

Application of YOLOv8 Image Recognition Model for Human Actions Recognition in the Surveillance Filed

Application of YOLOv8 Image Recognition Model for Human Actions Recognition in the Surveillance Filed

Image recognition model of fraudulent websites based on image leader decision and Inception-V3 transfer learning

Image recognition model of fraudulent websites based on image leader decision and Inception-V3 transfer learning

Research on the Application of Artificial Intelligence Technology in the Digital Processing of Traditional Visual Art Elements

Abstract The digital era has brought unprecedented convenience and prosperity to art design. This project develops the application of artificial intelligence technology from the digitalization process of traditional visual art elements in two dimensions: image enhancement and image recognition. Combining the quantile algorithm and adaptive gamma correction technology, we have designed an image enhancement method based on AGC-Quantile for traditional elements. Based on the classification model of the MobileNet network and the target detection model based on YOLOv5, the traditional element image recognition model is proposed. Traditional element images containing multiple types of tattoos are selected as experimental objects, and the actual performance of this model is investigated through algorithm comparison and evaluation index analysis. The AGC-Quantile method processes different tattoo images with high quality and outperforms the comparison algorithms in different indexes (MSE>17, PSNR>23, and SSIM>0.82). The classification model based on the MobileNet network and the target detection model based on YOLOv5 is proposed to enhance traditional element images. -YOLOv5 has a classification accuracy and target detection accuracy of 83.2% and 87.6%, respectively, and improves 65.82% in time. According to the experiment, the AI-based model constructed in this paper has superior image enhancement and image recognition effects and can be utilized for the digitization of traditional visual art elements.

Application of industrial robot based on infrared sensor technology in monitoring of electrical equipment

Abstract In this paper, infrared sensor technology is applied to the monitoring of electrical equipment, and a mathematical model of infrared image processing is built. In addition, the infrared image recognition model of electrical equipment is combined with the improved CenterNet, and ResNeXt50, which has stronger feature extraction capability, is used as the backbone network of CenterNet to improve detection accuracy. An infrared dataset of electrical equipment is used to verify the effectiveness of the improved CenterNet model and the other two models. Finally, the faults in substation equipment are analyzed and studied for real cases applied in practical work as an example. The results show that the algorithm in this paper has an mAP value of 93.7% and a frame rate of 40/S, which can balance the detection accuracy and detection speed in the electrical equipment infrared scene. Compared to traditional electrical equipment detection methods, the detection method of infrared sensor imaging is more reliable, with a 90% accuracy rate, and the highest has reached 100%. The article analyzes the characteristics and relationship between the local heating and temperature increase of the fault point of the equipment concerned, summarizes the fault judgment method, and verifies the effectiveness of infrared sensor imaging technology.

Hybrid Improved Concave Matching Algorithm and ResNet Image Recognition Model

Hybrid Improved Concave Matching Algorithm and ResNet Image Recognition Model

Mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments.

Recent decades have witnessed the development of vision-based dietary assessment (VBDA) systems. These systems generally consist of three main stages: food image analysis, portion estimation, and nutrient derivation. The effectiveness of the initial step is highly dependent on the use of accurate segmentation and image recognition models and the availability of high-quality training datasets. Food image segmentation still faces various challenges, and most existing research focuses mainly on Asian and Western food images. For this reason, this study is based on food images from sub-Saharan Africa, which pose their own problems, such as inter-class similarity and dishes with mixed-class food. This work focuses on the first stage of VBDAs, where we introduce two notable contributions. Firstly, we propose mid-DeepLabv3+, an enhanced food image segmentation model based on DeepLabv3+ with a ResNet50 backbone. Our approach involves adding a middle layer in the decoder path and SimAM after each extracted backbone feature layer. Secondly, we present CamerFood10, the first food image dataset specifically designed for sub-Saharan African food segmentation. It includes 10 classes of the most consumed food items in Cameroon. On our dataset, mid-DeepLabv3+ outperforms benchmark convolutional neural network models for semantic image segmentation, with an mIoU (mean Intersection over Union) of 65.20%, representing a +10.74% improvement over DeepLabv3+ with the same backbone.

Deep Learning Image Recognition-Assisted Atomic Force Microscopy for Single-Cell Efficient Mechanics in Co-culture Environments.

Atomic force microscopy (AFM)-based force spectroscopy assay has become an important method for characterizing the mechanical properties of single living cells under aqueous conditions, but a disadvantage is its reliance on manual operation and experience as well as the resulting low throughput. Particularly, providing a capacity to accurately identify the type of the cell grown in co-culture environments without the need of fluorescent labeling will further facilitate the applications of AFM in life sciences. Here, we present a study of deep learning image recognition-assisted AFM, which not only enables fluorescence-independent recognition of the identity of single co-cultured cells but also allows efficient downstream AFM force measurements of the identified cells. With the use of the deep learning-based image recognition model, the viability and type of individual cells grown in co-culture environments were identified directly from the optical bright-field images, which were confirmed by the following cell growth and fluorescent labeling results. Based on the image recognition results, the positional relationship between the AFM probe and the targeted cell was automatically determined, allowing the precise movement of the AFM probe to the target cell to perform force measurements. The experimental results show that the presented method was applicable not only to the conventional (microsphere-modified) AFM probe used in AFM indentation assay for measuring the Young's modulus of single co-cultured cells but also to the single-cell probe used in AFM-based single-cell force spectroscopy (SCFS) assay for measuring the adhesion forces of single co-cultured cells. The study illustrates deep learning imaging recognition-assisted AFM as a promising approach for label-free and high-throughput detection of single-cell mechanics under co-culture conditions, which will facilitate unraveling the mechanical cues involved in cell-cell interactions in their native states at the single-cell level and will benefit the field of mechanobiology.

FaceBreach Alert: Multi-Modal Intrusion Detection emphasizing Face Recognition

Abstract: The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the muchstudied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software.

Evaluation of Image Recognition Models In Machine Learning

Evaluation of Image Recognition Models In Machine Learning

Automated Peripheral Smear Recognition and Classification of LGL Leukemia Using Machine Learning

Background: Machine learning can augment clinical decision support, especially in cases where complex diagnostic criteria are necessary. In making a diagnosis of LGL leukemia, a pathologist must review the peripheral smear thoroughly, which is a tedious process. Supervised machine learning has been successfully implemented in such cases by training on very large image-based datasets, followed by a corrective phase of updating the model weights and finally demonstrating a high degree of successful predictions in an independent dataset. We sought to assess whether a supervised machine learning model can automate histopathological image analysis and provide a degree of confidence in supporting a pathologist's manual review. Methods: We searched the published literature from 2016-2022 for high-definition peripheral smear images with confirmed cases. The samples were downloaded, pre-processed with the Segment Anything Model (SAM), and labeled as training, testing, or validation data. Non-pathological controls, defined as peripheral smears confirmed negative for LGLL, were also obtained from published literature and labeled manually. The neural network was written and trained on TensorFlow using the open-source Keras library with parallel threading and on a multicore GPU machine. The resulting predictive model had a binary classification task: Does the presented image have cells that resemble LGL cells, returning true or false. Every “true” label returned from the classifier also creates a stack of probable positives that need pathology review. Feedback from each pathologist review further updates the model weights to increase the probability of successful prediction in the future. Results: We screened 57 publications to obtain 163 high-definition peripheral smear images that had confirmed LGLL and 100 non-pathological smears. Two cohorts were created: 200 smears in a training cohort comprised of 100 LGLL smears and 100 non-pathological smears, and 63 smears in the validation cohort. To remain agnostic towards image orientation, we created 7 views of each smear, and 1400 total images underwent feature extraction using SAM. The resulting data was labeled and used for model training and our process is depicted visually below in Figure 1. During validation, our model accurately predicted LGL-cells in 56/63 cases with an 8% false positive rate, 2% false negatives and 2% unclassifiable. After updating model weights and re-training with Adaptive Boosting, the accuracy increased to 60/63 smears with a false-positive rate of 3%. Our trained classification model achieved an AUC of 0.8144. Conclusion: Supervised machine learning model with Adaptive Boosting can be used to label a peripheral smear image with a high likelihood of containing LGL cells and, in turn, help a pathologist review smears with higher priority in a timely fashion. Our initial training data was limited, which hindered the performance of our model; however, with a sufficiently large training dataset, we can drastically improve our accuracy. A more extensive training set with robust hardware can transform our image-recognition model into a foundational model where other clinical parameters can be added, and the final diagnostic probability will be based on multiple data streams. Our work takes a meaningful step in this direction, and future work at Karmanos Cancer Center will focus on extending our classifier to more sophisticated deep-learning algorithms. Our open-source platform will eventually support several niches in the future that are currently only served by commercial applications.

A Research Paper on Recent Lung Cancer Detection Techniques Using Deep Learning

Lung cellular breakdown is a devastating and elusive malignant development. It’s crucial to concentrate on quickly and accurately evaluating knobs. As a result, several methods have been created to identify lung cellular deterioration at an early stage. These research papers' distinct division calculations are coupled to a range of classifier approaches to perform image recognition to identify cellular breakdown in the lung knobs. The inception V3 model is an image recognition model that has been shown to attain greater accuracy on the Image Net dataset. The results of this analysis demonstrate that CT check images are more reliable for getting better results. , an With the proposed model with marker-controlled watershed division yields, accuracy of 90.38% and data validation of 95%.

Fault detection and diagnosis of energy system based on deep learning image recognition model under the condition of imbalanced samples

Faults in energy systems impact the reliability of the energy supply and cause energy waste. Data-driven fault detection and diagnosis (FDD) methods can detect and diagnose system faults by mining historical operational data. However, the quantitative imbalance between fault and normal samples degrades the performance of the FDD method. Therefore, this study proposes a novel deep learning-based FDD method under the condition of imbalanced samples that converts the time-series signals into an image signal to extract the timing and coupling features, and then applies the improved conditional variational autoencoder-generative adversarial network (CVAE-GAN) to generate fault samples for balancing the training sample set. Subsequently, according to the framework of image recognition, a two-dimensional convolutional neural network was adopted to identify the image samples and achieve FDD. The experimental results showed that, under the condition of imbalanced samples, the proposed method could increase diagnosis accuracy by an average of 5.71% compared with other common data-driven methods. After the samples were augmented with the improved CVAE-GAN, the accuracy improved by an average of 3.79%. Consequently, the feasibility and superiority of the proposed method were demonstrated.

A novel hybrid machine learning model for shopping trip estimation: A case study of Tehran, Iran

Online and offline shopping trips have a profound impact on various facets of urban life, including e-commerce, transportation systems, and sustainability. To assess the factors shaping consumers' decisions, we introduce a novel hybrid machine learning model that integrates the Gray Wolf Optimization (GWO) algorithm with a deep Convolutional Neural Network (CNN). This model is applied to predict shopping behavior based on a survey of 1000 active e-commerce users residing in areas 2 and 5 of Tehran. These individuals have made successful purchases through both online and offline services during the final 20 days of 2021. The GWO algorithm plays a pivotal role in selecting optimal features and hyperparameters for the deep Convolutional Neural Network, which is a powerful deep learning model for image recognition and classification. Notably, our model achieves an impressive accuracy of 97.81% while maintaining a MSE of 0.325, having identified seven out of ten key features as the most influential. To gage the effectiveness of our approach, we conduct a comparative analysis with alternative methods. The results unequivocally showcase the superiority of our proposed algorithm, which attains an accuracy of 97.81%. In contrast, other models such as CNN, LSTM, MLP, DT, and KNN yield accuracies of 95.63%, 94.04%, 90.12%, 86.49%, and 80.16%, respectively. This study offers valuable insights for transportation planners, e-commerce managers, and policymakers. Its primary objective is to assist them in formulating effective strategies aimed at reducing transportation costs, curbing pollutant emissions, mitigating urban traffic congestion, and enhancing user satisfaction all while fostering sustainable development.

Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages

Artificial intelligence models are currently being proposed for application in improving performance in addressing contemporary management and production issues. With the goal of automating the detection of road surface defects in transportation infrastructure management to make it more convenient, this research harnesses the advancements of the latest artificial intelligence models. Notably, new technology is used in this study to develop software that can automatically detect road surface damage, which shall lead to better results compared to previous models. This study evaluates and compares machine learning models using the same dataset for model training and performance assessment consisting of 9053 images from previous research. Furthermore, to demonstrate practicality and superior performance over previous image recognition models, mAP (mean average precision) and processing speed, which are recognized as a measure of effectiveness, are employed to assess the performance of the machine learning object recognition software models. The results of this research reveal the potential of the new technology, YOLO V5 (2023), as a high-performance model for object detection in technical transportation infrastructure images. Another significant outcome of the research is the development of an improved software named RTI-IMS, which can apply automation features and accurately detect road surface damages, thereby aiding more effective management and monitoring of sustainable road infrastructure.

An AI Dietitian for Type 2 Diabetes Mellitus Management Based on Large Language and Image Recognition Models: Preclinical Concept Validation Study.

Nutritional management for patients with diabetes in China is a significant challenge due to the low supply of registered clinical dietitians. To address this, an artificial intelligence (AI)-based nutritionist program that uses advanced language and image recognition models was created. This program can identify ingredients from images of a patient's meal and offer nutritional guidance and dietary recommendations. The primary objective of this study is to evaluate the competence of the models that support this program. The potential of an AI nutritionist program for patients with type 2 diabetes mellitus (T2DM) was evaluated through a multistep process. First, a survey was conducted among patients with T2DM and endocrinologists to identify knowledge gaps in dietary practices. ChatGPT and GPT 4.0 were then tested through the Chinese Registered Dietitian Examination to assess their proficiency in providing evidence-based dietary advice. ChatGPT's responses to common questions about medical nutrition therapy were compared with expert responses by professional dietitians to evaluate its proficiency. The model's food recommendations were scrutinized for consistency with expert advice. A deep learning-based image recognition model was developed for food identification at the ingredient level, and its performance was compared with existing models. Finally, a user-friendly app was developed, integrating the capabilities of language and image recognition models to potentially improve care for patients with T2DM. Most patients (182/206, 88.4%) demanded more immediate and comprehensive nutritional management and education. Both ChatGPT and GPT 4.0 passed the Chinese Registered Dietitian examination. ChatGPT's food recommendations were mainly in line with best practices, except for certain foods like root vegetables and dry beans. Professional dietitians' reviews of ChatGPT's responses to common questions were largely positive, with 162 out of 168 providing favorable reviews. The multilabel image recognition model evaluation showed that the Dino V2 model achieved an average F1 score of 0.825, indicating high accuracy in recognizing ingredients. The model evaluations were promising. The AI-based nutritionist program is now ready for a supervised pilot study.