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A review of deep learning in blink detection

PEERJ COMPUTER SCIENCE 2025年 11卷 e2594-e2594页

作者： Xiong, Jianbin Dai, Weikun Wang, Qi Dong, Xiangjun Ye, Baoyu Yang, Jianxiang Guangdong Polytech Normal Univ Sch Automat Guangzhou Guangdong Peoples R China Guangzhou Civil Aviat Coll Sch Aircraft Maintenance Engn Guangzhou Guangdong Peoples R China

Blink detection is a highly concerned research direction in the field of computer vision, which plays a key role in various application scenes such as human-computer interaction, fatigue detection and emotion perception. In recent years, with the rapid development of deep learning, the application of deep learning techniques for precise blink detection has emerged as a significant area of interest among researchers. Compared with traditional methods, the blink detection method based on deep learning offers superior feature learning ability and higher detection accuracy. However, the current research on blink detection based on deep learning lacks systematic summarization and comparison. Therefore, the aim of this article is to comprehensively review the research progress in deep learning-based blink detection methods and help researchers to have a clear understanding of the various approaches in this field. This article analyzes the progress made by several classical deep learning models in practical applications of eye blink detection while highlighting their respective strengths and weaknesses. Furthermore, it provides a comprehensive summary of commonly used datasets and evaluation metrics for blink detection. Finally, it discusses the challenges and future directions of deep learning for blink detection applications. Our analysis reveals that deep learning-based blink detection methods demonstrate strong performance in detection. However, they encounter several challenges, including training data imbalance, complex environment interference, real-time processing issues and application device limitations. By overcoming the challenges identified in this study, the application prospects of deep learning-based blink detection algorithms will be significantly enhanced.

关键词： Blink detection deep learning Feature extraction Transfer learning

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Reducing the Counting time of Colonies Using image processing Techniques 24

Reducing the Counting Time of Colonies Using Image Processin...

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24th International Conference on Control, Automation and Systems

作者： Siangchin, Apichai Ploysuwan, Tuchsanai King Mongkuts Inst Technol Ladkrabang Sch Informat Technol Bangkok Thailand

ISBN: (纸本)9798331517939;9788993215380

Microbial colony counting is a crucial process in microbiology laboratories and medical research, yet it is often time-consuming and labor-intensive, particularly in large-scale settings or when dealing with numerous samples. Incorporating image processing techniques can significantly reduce the time and error associated with manual colony counting. In this study, we developed and evaluated a deep learning model for automated E. coli colony counting using the YOLO (You Only Look Once) framework. The model achieved an mAP50 of 90.3%, demonstrating its high accuracy and potential for real-world application in laboratories of various sizes. This research not only streamlines the colony counting process but also allows laboratory personnel to allocate their time to other essential tasks.

关键词： YOLO colony counting image processing deep learning

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Advanced deep learning Models for real-time image Recognition: Bridging the Gap between Accuracy and Speed 5

Advanced Deep Learning Models for Real-Time Image Recognitio...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Maheshwari, Himani Sachdeva, Shaweta Yadav, Dharminder Chandra, Umesh Tiwari, Raj Gaurang Gupta, Ashulekha Graphic Era Hill University School of Computing Uttrakhand Dehradun India Guru Nanak Khalsa College Department of Computer Science and Applications Haryana Yamuna Nagar India Gmssss Haryana Chharauli India CoA Buat Department of Statistics and Computer Science Uttar Pradesh Banda India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Deemed to be University Department of Management Dehradun India

ISBN: (纸本)9798350376685

Cutting-edge deep learning algorithms have improved image recognition. This study aims to balance real-time image identification speed and accuracy. Analysis of deep-learning architectures and challenge-specific optimization approaches do this. This work uses deep learning and edge computing to enhance real-time image recognition. The technique suggests using the successful InceptionV3 architecture to train on the CelebA dataset of 202,599 annotated celebrity images. In this work, thorough preprocessing and feature extraction guarantee high-quality input data. Impressively, the InceptionV3 model has an F1 score of 0.96, precision of 0.97, recall of 0.98, and accuracy of 0.96. Edge computing speeds up processing and response times. The algorithm correctly distinguishes faces in every context, according to the results. By merging deep learning models with edge computer networks, real-time image recognition system performs greatly. © 2024 IEEE.

关键词： Contrastive learning

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A lightweight intrusion detection algorithm for IoT based on data purification and a separable convolution improved CNN

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KNOWLEDGE-BASED SYSTEMS 2024年 304卷

作者： Yang, Tao Chen, Jiangchuan Deng, Hongli He, Baolin China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China China West Normal Univ Sch Elect & Informat Engn Nanchong 637002 Peoples R China

With the rapid development of the IoT (Internet of Things), the network data present the characteristics of large volume and high dimension. Convolutional neural networks (CNNs) have become one of the most important intrusion detection methods due to their advantages in processing high-dimensional data. The conventional intrusion detection model based on CNN lacks an effective data purification means in the process of converting unstructured data into image data, and too many parameters are generated due to the complex structure of the model in the training process, leading to the problems of high time complexity and low detection rate of the model, which limits the application of the CNN in intrusion detection of the IoT. First, based on the principle of liquid molecular distillation, a data purification algorithm (DPA) for unstructured data is proposed in this paper, which reduces the "redundant" data generated in the process of converting unstructured data to image data. Second, based on the rigid-motion convolution principle of a separable wavelet, separable convolution is used to improve the CNN structure, and then a lightweight detection algorithm LSCNN (lightweight CNN based on separable convolution) is developed to reduce the number of parameters in the network structure and improve the time efficiency and accuracy of the algorithm. The experimental results on real intrusion detection datasets show that the LSCNN trained on DPA purified data has higher time efficiency and detection accuracy than the conventional CNN, and compared with the conventional machine learning algorithm, it has higher accuracy.

关键词： IDS IoT deep learning CNN

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deep learning based insulator fault detection algorithm for power transmission lines

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JOURNAL OF real-time image processing 2024年第4期21卷 115-115页

作者： Wang, Han Yang, Qing Zhang, Binlin Gao, Dexin Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266061 Peoples R China

Aiming at the complex background of transmission lines at the present stage, which leads to the problem of low accuracy of insulator fault detection for small targets, a deep learning-based insulator fault detection algorithm for transmission lines is proposed. First, aerial images of insulators are collected using UAVs in different scenarios to establish insulator fault datasets. After that, in order to improve the detection efficiency of the target detection algorithm, certain improvements are made on the basis of the YOLOV9 algorithm. The improved algorithm enhances the feature extraction capability of the algorithm for insulator faults at a smaller computational cost by adding the GAM attention mechanism;at the same time, in order to realize the detection efficiency of small targets for insulator faults, the generalized efficient layer aggregation network (GELAN) module is improved and a new SC-GELAN module is proposed;the original loss function is replaced by the effective intersection-over-union (EIOU) loss function to minimize the difference between the aspect ratio of the predicted frame and the real frame, thereby accelerating the convergence speed of the model. Finally, the proposed algorithm is trained and tested with other target detection algorithms on the established insulator fault dataset. The experimental results and analysis show that the algorithm in this paper ensures a certain detection speed, while the algorithmic model has a higher detection accuracy, which is more suitable for UAV fault detection of insulators on transmission lines.

关键词： YOLOv9 Convolutional neural network Object detection Transmission lines

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Efficient deep Model-Based Optoacoustic image Reconstruction

Efficient Deep Model-Based Optoacoustic Image Reconstruction

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2024 IEEE Ultrasonics, Ferroelectrics, and Frequency Control Joint Symposium

作者： Dehner, Christoph Zahnd, Guillaume iThera Med GmbH Munich Germany

ISBN: (纸本)9798350371918;9798350371901

Clinical adoption of multispectral optoacoustic tomography necessitates improvements of the image quality available in real-time, as well as a reduction in the scanner financial cost. deep learning approaches have recently unlocked the reconstruction of high-quality optoacoustic images in real-time. However, currently used deep neural network architectures require powerful graphics processing units to infer images at sufficiently high frame-rates, consequently greatly increasing the price tag. Herein we propose EfficientdeepMB, a relatively lightweight (17M parameters) network architecture achieving high frame-rates on medium-sized graphics cards with no noticeable downgrade in image quality. EfficientdeepMB is built upon deepMB, a previously established deep learning framework to reconstruct high-quality images in real-time, and upon EfficientNet, a network architectures designed to operate of mobile devices. We demonstrate the performance of EfficientdeepMB in terms of reconstruction speed and accuracy using a large and diverse dataset of in vivo optoacoustic scans. EfficientdeepMB is about three to five times faster than deepMB: deployed on a medium-sized NVIDIA RTX A2000 Ada, EfficientdeepMB reconstructs images at speeds enabling live image feedback (59 Hz) while deepMB fails to meets the real-time inference threshold (14 Hz). The quantitative difference between the reconstruction accuracy of EfficientdeepMB and deepMB is marginal (data residual norms of 0.1560 vs. 0.1487, mean absolute error of 0.642 vs. 0.745). There are no perceptible qualitative differences between images inferred with the two reconstruction methods.

关键词： Optoacoustic imaging deep neural networks Model-based reconstruction real-time imaging Computational efficiency

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Frequency Estimation Using Complex-Valued Shifted Window Transformer

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Smith, Josiah W. Torlak, Murat Univ Texas Dallas Elect & Comp Engn Dept Richardson TX 75080 USA

Estimating closely spaced frequency components of a signal is a fundamental problem in statistical signal processing. In this letter, we introduce 1-D real-valued and complex-valued shifted window (Swin) transformers, referred to as SwinFreq and CVSwinFreq, respectively, for line-spectra frequency estimation on 1-D complex-valued signals. Whereas 2-D Swin transformer-based models have gained traction for optical image super-resolution (SR), we introduce for the first time a complex-valued Swin module designed to leverage the complex-valued nature of signals for a wide array of applications. The proposed approach overcomes the limitations of the classical algorithms and the state-of-the-art deep learning approach cResFreq. SwinFreq and CVSwinFreq boast superior performance at low signal-to-noise ratio (SNR) and improved resolution capability while requiring fewer model parameters than cResFreq, deeming them more suitable for edge and mobile applications. SwinFreq achieves an average improvement of 0.03 in structural similarity index measure (SSIM) over cResFreq and a 5% gain in probability of resolution for closely spaced frequency components. We find that the real-valued Swin-Freq outperforms its complex-valued counterpart CVSwinFreq for several tasks while touting a smaller model size. Finally, we apply the proposed techniques for radar range profile SR using real data. The results from both synthetic and real experimentation validate the numerical and empirical superiority of SwinFreq and CVSwinFreq to the state-of-the-art deep-learning-based techniques and traditional frequency estimation algorithms. The code and models are publicly available at https://***/josiahwsmith10/spectral-super-resolution-swin.

关键词： Complex-valued neural network (CVNN) deep learning frequency estimation radar spectral super-resolution (SR) Swin transformer

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CIR-DFENet: Incorporating cross-modal image representation and dual-stream feature enhanced network for activity recognition

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Zhao, Yuliang Shao, Jinliang Lin, Xiru Sun, Tianang Li, Jian Lian, Chao Lyu, Xiaoyong Si, Binqiang Zhan, Zhikun Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China Hebei Key Lab Micronano Precis Opt Sensing & Measu Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Beijing Informat Sci & Technol Univ Beijing 100192 Peoples R China

Human activity recognition (HAR) based on wearable sensors has been widely used in various fields such as health monitoring, healthcare, and fitness due to its portability, accuracy, and real-time capabilities. Currently, advanced technologies involve converting time-series into images and deep learning for recognition, which addresses issues of subjectivity and dependence on data quality inherent in traditional processing methods. However, each of the current methods for converting time-series data into images only focuses on one type of feature representation such that one of these methods is insufficient to fully characterize the data, which results in lower accuracy inactivity recognition. To address the above problems, we propose a novel method for cross-modal image representation of time-series and a dual-stream feature enhanced network model, which enables HAR based on a single-node wearable sensor. Firstly, three methods including Markov Transition Field (MTF), Recurrence Plot (RP), and Gramian Angular Field (GAF), are employed to encode the time-series into three channels (R, G, B) of color images, facilitating the fusion of features such as amplitude variation, nonlinearity, and local temporal relationship. Then, a multi-channel CNN with a Global Attention Mechanism (GAM) is employed in the model to process images, which captures both channel and spatial information. A network combining CNN and Long Short-Term Memory (LSTM) with Self-Attention Mechanism (SA) is utilized to process time-series, which captures temporal features. Simultaneously, the introduction of residual structures makes the network easy to train, thereby enhancing overall performance. The experimental results demonstrate that the proposed model can accurately identify six different gymnastic activities, achieving an accuracy rate of 99.40%. This study provides anew direction for processing time-series and offers better applications in the field of HAR based on wearable sensors.

关键词： Human activity recognition Wearable sensor Imaging deep learning

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SECURE MEDICAL image RETRIEVAL USING FAST image processing ALGORITHMS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 4323-4334页

作者： Lafta, Sameer Abdulsttar Rafash, Amaal Ghazi Hamad Al-falahi, Noaman Ahmed Yaseen Hussein, Hussein Abdulqader Abdulkareem, Mohanad Mahdi Middle Tech Univ Tech Instructors Training Inst Baghdad Iraq Iraqi Minist Commun Digital Transformat Dept Baghdad Iraq Iraqi Minist Educ Data Ctr Management Dept Baghdad Iraq

Content Based image Retrieval (CBIR) is a relatively new idea in the field of real-time image retrieval applications;it is a framework for retrieving pictures from diverse medical imaging sources using a variety of image-related attributes, such as color, texture, and form. Using both single and multiple input queries, CBIR processes semantic data or the same object for various class labels in the context of medical image retrieval. Due to the ambiguity of image search, optimizing the retrieval of a query picture by comparing it across numerous image sources may be problematic. The goal is to find a way to optimize the process by which requested images are retrieved from various storage locations. To effectively extract medical images, we propose a hybrid framework (consisting of deep convolution neural networks (DCNN) and the Pareto Optimization technique). In order to obtain medical pictures, a DCNN is trained on them, and then its properties and classification results are employed. Explore enhanced effective medical picture retrieval by using a Pareto optimization strategy to eliminate superfluous and dominant characteristics. When it comes to retrieving images by query from various picture archives, our method outperforms more conventional methods. Use the jargon of machine learning to propose a Novel Unsupervised Label Indexing (NULI) strategy for retrieving picture labels. To enhance the effectiveness of picture retrieval, we characterize machine learning as a matrix convex optimization using a cluster rebased matrix representation. We describe an empirical investigation on many medical picture datasets, finding that the searchbased image annotation (SBIA) schema benefits from our suggested method. As a result, CT images of the lung region are explored in this study by constructing a content-based image retrieval system using various machine learning and Artificial Intelligence techniques. real-world applications of medical imaging are becoming more significan

关键词： Medical image image retrieval image processing

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Non-Intrusive Water Surface Velocity Measurement Based on deep learning

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WATER 2024年第19期16卷 2784页

作者： An, Guocheng Du, Tiantian He, Jin Zhang, Yanwei Shanghai Huaxun Network Syst Co Ltd Artificial Intelligence Res Inst Chengdu 610074 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Accurate assessment of water surface velocity (WSV) is essential for flood prevention, disaster mitigation, and erosion control within hydrological monitoring. Existing image-based velocimetry techniques largely depend on correlation principles, requiring users to input and adjust parameters to achieve reliable results, which poses challenges for users lacking relevant expertise. This study presents RivVideoFlow, a user-friendly, rapid, and precise method for WSV. RivVideoFlow combines two-dimensional and three-dimensional orthorectification based on Ground Control Points (GCPs) with a deep learning-based multi-frame optical flow estimation algorithm named VideoFlow, which integrates temporal cues. The orthorectification process employs a homography matrix to convert images from various angles into a top-down view, aligning the image coordinates with actual geographical coordinates. VideoFlow achieves superior accuracy and strong dataset generalization compared to two-frame RAFT models due to its more effective capture of flow velocity continuity over time, leading to enhanced stability in velocity measurements. The algorithm has been validated on a flood simulation experimental platform, in outdoor settings, and with synthetic river videos. Results demonstrate that RivVideoFlow can robustly estimate surface velocity under various camera perspectives, enabling continuous real-time dynamic measurement of the entire flow field. Moreover, RivVideoFlow has demonstrated superior performance in low, medium, and high flow velocity scenarios, especially in high-velocity conditions where it achieves high measurement precision. This method provides a more effective solution for hydrological monitoring.

关键词： water surface velocity RivVideoFlow multi-frame optical flow deep learning orthorectification video processing

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