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A one-shot face detection and recognition using deep learning method for access control system

SIGNAL image AND VIDEO processing 2023年第4期17卷 1571-1579页

作者： Tsai, Tsung-Han Tsai, Chi-En Chi, Po-Ting Natl Cent Univ Dept Elect Engn 300 Jung Da Rd Zhongli 320 Taiwan

In this paper, we propose a face detection and recognition system using deep learning method. It can be used as an access control system that performs face detection and recognition in real-time processing. Our goal is to achieve a one-shot recognition instead of traditional two-step methods. We use SSD as the main model for face detection and VGG-Face as the main model for face recognition. We perform the deep learning method through the collection of datasets. Moreover, we use some techniques, such as data augmentation, preprocessing of the image, and post-processing of the image to train the robust face detection and recognition subsystems. We use continuous frames as input to avoid false-positive cases and make the system output without wrong results. A real demonstration system is constructed to determine the identification of the laboratory members. We use 1280 x 960 resolution video for experimental testing and achieve about 30 fps speed under GPU acceleration.

关键词： Face detection Face recognition deep neural network Machine learning Artificial intelligence

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Supervised deep learning based Leaf Disease and Pest Detection using image processing

Supervised Deep Learning based Leaf Disease and Pest Detecti...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Deva Priya Isravel Kumaresan Somasundaram M Jestin Josephraj L Christopher Paul Jp Johnson Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Sivaganga Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Kumbakonam Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Dharmapuri Tamil Nadu India

The key problems that influence plant health and crop yield quality are leaf disease and pests. To improve crop production many advanced technologies are deployed. One of the predominant technologies is the incorporation of deep learning (DL) techniques. DL supports numerous training and testing models that are suitable for smart agriculture. real-time detection of plant diseases and pest detection is made simpler and more efficient by using DL models. This paper provides an extensive survey of the DL techniques associated with agriculture automation and discusses the latest models focusing on leaf disease detection and pest identification. To select the best features and to optimize the accuracy of the results, an optimizer is identified and an enhanced deep learning model is proposed. The VGG16 and YOLOV5s models are deployed with ADAM optimizer. The results illustrate that the proposed optimized approach achieves an accuracy of 98.71% for leaf disease detection and 97.52% for pest detection.

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real-time Self-Supervised Ultrasound image Enhancement Using Test-time Adaptation for Sophisticated Rotator Cuff Tear Diagnosis

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IEEE SIGNAL processing LETTERS 2025年 32卷 1635-1639页

作者： Lee, Haeyun Lee, Kyungsu Yoon, Jong Pil Kim, Jihun Kim, Jun-Young Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan 31253 South Korea Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju 54896 South Korea Kyungpook Natl Univ Sch Med Dept Orthoped Surg Daegu 41566 South Korea Kangnam Univ Div Elect & Semicond Engn Elect Engn Yongin 16979 South Korea Catholic Univ Daegu Sch Med Dept Orthoped Surg Daegu 42472 South Korea

Medical ultrasound imaging is a key diagnostic tool across various fields, with computer-aided diagnosis systems benefiting from advances in deep learning. However, its lower resolution and artifacts pose challenges, particularly for non-specialists. The simultaneous acquisition of degraded and high-quality images is infeasible, limiting supervised learning approaches. Additionally, self-supervised and zero-shot methods require extensive processing time, conflicting with the real-time demands of ultrasound imaging. Therefore, to address the aforementioned issues, we propose real-time ultrasound image enhancement via a self-supervised learning technique and a test-time adaptation for sophisticated rotational cuff tear diagnosis. The proposed approach learns from other domain image datasets and performs self-supervised learning on an ultrasound image during inference for enhancement. Our approach not only demonstrated superior ultrasound image enhancement performance compared to other state-of-the-art methods but also achieved an 18% improvement in the RCT segmentation performance.

关键词： Ultrasonic imaging image restoration Training real-time systems Self-supervised learning Biomedical imaging image enhancement Superresolution image resolution Medical diagnostic imaging Ultrasound image image enhancement test time adaptation rotator cuff tear

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Cross-task cognitive workload estimation using eye tracking

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SIGNAL image AND VIDEO processing 2025年第5期19卷

作者： Yang, Lin Wang, Lei Xu, Wenchang Wang, Biao Ren, Hanbin Yang, Aijuan Univ Sci & Technol China Sch Biomed Engn Suzhou Div Life Sci & Med Hefei 230026 Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol CAS Key Lab Biomed Diagnost Suzhou 215163 Peoples R China Suzhou ZhongKe Med Device Ind Dev Co Ltd Suzhou 215163 Peoples R China Jinan Guoke Med Technol Dev Co Ltd Jinan 250001 Peoples R China

Cognitive workload is a key factor in understanding human cognitive performance, especially in scenarios that require intensive information processing. This study introduces an innovative method to estimate cognitive workload using eye-tracking data and proposes a novel deep learning model called BiTCADNet (Bidirectional Temporal Convolutional self-Attention Dense Network). Experiments using the newly created dataset "Cognitive-Eye-Movement" and the publicly available dataset "CL-Drive" show that BiTCADNet significantly outperforms traditional deep learning models in terms of accuracy, precision, recall, and F1 scores are significantly better than traditional machine learning methods. The proposed method provides a more effective way to monitor and evaluate cognitive workload in real-time, opening the way for its applications in various human-computer interaction environments.

关键词： deep-learning Cognitive-workload Eye-tracking Self-attention mechanism

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Needle tracking in low-resolution ultrasound volumes using deep learning

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2024年第10期19卷 1975-1981页

作者： Grube, Sarah Latus, Sarah Behrendt, Finn Riabova, Oleksandra Neidhardt, Maximilian Schlaefer, Alexander Hamburg Univ Technol Inst Med Technol & Intelligent Syst Hamburg Germany

PurposeClinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US *** design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle *** experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep *** study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.

关键词： Volumetric ultrasound imaging deep learning Needle tip detection real-time Sparse feature learning

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A real-time foreign object detection method based on deep learning in complex open railway environments

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JOURNAL OF real-time image processing 2024年第5期21卷 166页

作者： Zhang, Binlin Yang, Qing Chen, Fengkui 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

In response to the current challenges of numerous background influencing factors and low detection accuracy in the open railway foreign object detection, a real-time foreign object detection method based on deep learning for open railways in complex environments is proposed. Firstly, the images of foreign objects invading the clearance collected by locomotives during long-term operation are used to create a railway foreign object dataset that fits the current situation. Then, to improve the performance of the target detection algorithm, certain improvements are made to the YOLOv7-tiny network structure. The improved algorithm enhances feature extraction capability and strengthens detection performance. By introducing a Simple, parameter-free Attention Module for convolutional neural network (SimAM) attention mechanism, the representation ability of ConvNets is improved without adding extra parameters. Additionally, drawing on the network structure of the weighted Bi-directional Feature Pyramid Network (BiFPN), the backbone network achieves cross-level feature fusion by adding edges and neck fusion. Subsequently, the feature fusion layer is improved by introducing the GhostNetV2 module, which enhances the fusion capability of different scale features and greatly reduces computational load. Furthermore, the original loss function is replaced with the Normalized Wasserstein Distance (NWD) loss function to enhance the recognition capability of small distant targets. Finally, the proposed algorithm is trained and validated, and compared with other mainstream detection algorithms based on the established railway foreign object dataset. Experimental results show that the proposed algorithm achieves applicability and real-time performance on embedded devices, with high accuracy, improved model performance, and provides precise data support for railway safety assurance.

关键词： YOLOv7-tiny Object detection deep learning Complex environment

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Fast no-reference deep image dehazing

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MACHINE VISION AND APPLICATIONS 2024年第5期35卷 122页

作者： Qin, Hongyi Belyaev, Alexander G. Imperial Coll London Dept Elect & Elect Engn London England Heriot Watt Univ Sch Engn & Phys Sci Inst Sensors Signals & Syst Edinburgh Scotland

This paper presents a deep learning method for image dehazing and clarification. The main advantages of the method are high computational speed and using unpaired image data for training. The method adapts the Zero-DCE approach (Li et al. in IEEE Trans Pattern Anal Mach Intell 44(8):4225-4238, 2021) for the image dehazing problem and uses high-order curves to adjust the dynamic range of images and achieve dehazing. Training the proposed dehazing neural network does not require paired hazy and clear datasets but instead utilizes a set of loss functions, assessing the quality of dehazed images to drive the training process. Experiments on a large number of real-world hazy images demonstrate that our proposed network effectively removes haze while preserving details and enhancing brightness. Furthermore, on an affordable GPU-equipped laptop, the processing speed can reach 1000 FPS for images with 2K resolution, making it highly suitable for real-time dehazing applications.

关键词： image dehazing and clarification No-reference neural network training real-time dehazing Zero-DCE

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FieldNet: Efficient real-time shadow removal for enhanced vision in field robotics

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Saleh, Alzayat Olsen, Alex Wood, Jake Philippa, Bronson Azghadi, Mostafa Rahimi James Cook Univ Coll Sci & Engn Townsville Qld Australia AutoWeed Pty Ltd Townsville Qld Australia James Cook Univ ARC Res Hub Super charging Trop Aquaculture Genet Townsville Qld Australia

Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and localization. We present FieldNet, a novel deep learning framework for real-time shadow removal, optimized for resource-constrained hardware. FieldNet introduces a probabilistic enhancement module and a novel loss function to address challenges of inconsistent shadow boundary supervision and artefact generation, achieving enhanced accuracy and simplicity without requiring shadow masks during inference. Trained on a dataset of 10,000 natural images augmented with synthetic shadows, FieldNet outperforms state-of-the-art methods on benchmark datasets (ISTD, ISTD+, SRD), with up to 9x speed improvements (66 FPS on Nvidia 2080Ti) and superior shadow removal quality (PSNR: 38.67, SSIM: 0.991). real-world case studies in precision agriculture robotics demonstrate the practical impact of FieldNet in enhancing weed detection accuracy. These advancements establish FieldNet as a robust, efficient solution for real-time vision tasks in field robotics and beyond.

关键词： Shadow removal Unpaired data real-time image processing deep learning Field robotics

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Yolo-global: a real-time target detector for mineral particles

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JOURNAL OF real-time image processing 2024年第3期21卷 85-85页

作者： Wang, Zihao Zhou, Dong Guo, Chengjun Zhou, Ruihao Univ Elect Sci & Technol China 2006 Xiyuan Ave Chengdu Peoples R China Chongqing Changan Ind Grp Co Ltd 599 Airport Ave Chongqing Peoples R China

Recently, deep learning methodologies have achieved significant advancements in mineral automatic sorting and anomaly detection. However, the limited features of minerals transported in the form of small particles pose significant challenges to accurate detection. To address this challenge, we propose a enhanced mineral particle detection algorithm based on the YOLOv8s model. Initially, a C2f-SRU block is introduced to enable the feature extraction network to more effectively process spatial redundant information. Additionally, we designed the GFF module with the aim of enhancing information propagation between non-adjacent scale features, thereby enabling deep networks to more fully leverage spatial positional information from shallower networks. Finally, we adopted the Wise-IoU loss function to optimize the detection performance of the model. We also re-designed the position of the prediction heads to achieve precise detection of small-scale targets. The experimental results substantiate the effectiveness of the algorithm, with YOLO-Global achieving a mAP@.5 of 95.8%. In comparison to the original YOLOv8s, the improved model exhibits a 2.5% increase in mAP, achieving a model inference speed of 81 fps, meeting the requirements for real-time processing and accuracy.

关键词： Small target detection Lightweight deep learning Computer vision

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License Plate reader with PUC Details using image processing and deep learning

License Plate reader with PUC Details using Image Processing...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： R.S. Patil Sakshi Deshpande Hassan Khan Prajakta Mhatre Department of Electronics and Telecommunication Engineering Bharati Vidyapeeth College of Engineering Belpada Navi Mumbai Maharashtra India

As each vehicle is uniquely acknowledged by its license plate, the Transport System places a high priority on finding and recognizing of license plates. The news is constantly reporting on accidents and missing cars. Authorities must acknowledge all of these unlawful acts. As a result, research into the identification and recognition of vehicle number plates is ongoing. However, identifying a vehicle’s number plate has always been difficult for a number of reasons, such as brightness changes, shadows cast by moving vehicles, erratic license plate character types, different plate styles, and color effects caused by the surroundings. In this system, Number plate of vehicle is detected from a live video or an image. There is image preprocessing and segmentation done on the live video or number plate image. deep learning model methods are used, the characters from it are separated and then each character gets recognized. This helps to collect the vehicle overall project then the capabilities of different techniques into one integrated automatic system are summarized. This kind of systems can be implemented on the roadside and makes a real time comparison between passing car and list of stolen cars. This detected license plate number could also be used in car parking systems. PUC which stands for Pollution Under Control, where emission levels of vehicles and the regular renovation of the PUC certificate is done or not is verified and the details are shown. This will help in keeping an overall check on vehicles and the task which most of the places do manually to check the PUC certificate for checking status, can be verified quickly and the fine can be implemented as per so.

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