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real-time masked face recognition using deep learning-based double generator network

SIGNAL image AND VIDEO processing 2024年第SUPPL 1期18卷 325-334页

作者： Sumathy, G. Usha, M. Rajakumar, S. Jayapriya, P. SRM Inst Sci & Technol Dept Computat Intelligence Chennai 603203 Tamil Nadu India MEASI Inst Informat Technol Dept Masters Comp Applicat Chennai Tamilnadu India Panimalar Engn Coll Dept Elect & Commun Engn Chennai 600123 India Sri Eshwar Engn Coll Ctr Future Networks & Digital Twin Dept Comp Sci & Engn Coimbatore India

The COVID-19 outbreak has spread rapidly worldwide since 2019. This pandemic has complicated and intricate human existence, and thousands have died from it. Due to the spread of coronavirus, people wear masks while going outside. Consequently, the system cannot identify their faces while wearing the masks. This issue can be overcome by introducing a system that recognizes masked faces of random people trained with 100 images taken from the Internet. This paper presents a novel deep learning-based double generator network to precisely identify the face behind the mask images. Initially, the gathered images are split into low- and high-frequency components using 2D-stationary wavelet transform (2D-SWT). Afterward, the Haar cascade classifier was implemented to capture the masked image biometrics to recognize the individual faces. The proposed double generator network involves two modules: edge generation and image reconstruction. The first modules consist of dilated convolutional for retrieving the relevant features from the masked face images created on the generated edges. The generated edges are reconstructed using the reflection of generated edges in the second module. Finally, the output images are reconstructed to identify the masked face. From the simulation results, the proposed framework showed effective performance based on the network parameters. The proposed network attains an accuracy of 97.08% for masked face recognition which demonstrates it achieves higher accuracy than the prior frameworks.

关键词： Masked face recognition deep learning Stationary wavelet transform Haar cascade classifier Double generator network

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LGFF-YOLO: small object detection method of UAV images based on efficient local-global feature fusion

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JOURNAL OF real-time image processing 2024年第5期21卷 167页

作者： Peng, Hongxing Xie, Haopei Liu, Huanai Guan, Xianlu South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China South China Technol Univ Sch Chem & Chem Engn Guangzhou 510641 Peoples R China South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China

images captured by Unmanned Aerial Vehicles (UAVs) play a significant role in many fields. However, with the development of UAV technology, challenges such as detecting small and dense objects against complex backgrounds have emerged. In this paper, we propose LGFF-YOLO, a detection model that integrates a novel local-global feature fusion method with the YOLOv8 baseline, specifically designed for small object detection in UAV imagery. Our innovative approach employs the Global Information Fusion Module (GIFM) and the Four-Leaf Clover Fusion Module (FLCM) to enhance the fusion of multi-scale features, improving detection accuracy without increasing model complexity. Next, we proposed the RFA-Block and LDyHead to control the total number of model parameters and improve the representation capability for small object detection. Experimental results on the VisDrone2019 dataset demonstrate a 38.3% mAP with only 4.15M parameters, a 4. 5% increase over baseline YOLOv8, while achieving 79.1 FPS for real-time detection. These advancements enhance the model's generalization capability, balancing accuracy and speed, and significantly extend its applicability for detecting small objects in UAV images.

关键词： Small object detection Feature confusion real-time detection deep learning

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Detecting Halyomorpha halys using a low-power edge-based monitoring system

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 221卷

作者： Kargar, Amin Zorbas, Dimitrios Tedesco, Salvatore Gaffney, Michael O'Flynn, Brendan Univ Coll Cork Tyndall Natl Inst Cork Ireland Nazarbayev Univ Sch Engn & Digital Sci Astana Kazakhstan Teagasc Ashtown Food Res Ctr Hort Dev Dept Dublin Ireland

Smart monitoring systems in orchards can automate agriculture monitoring processes and provide useful information about the presence of insects, such as the Brown Marmorated Stink Bug (BMSB), that threaten the production quantity and quality of fruit such as pears. Unlike other approaches in the literature, we propose a low-cost image monitoring system which exhibits a very low power consumption without compromising much of the accuracy that existing expensive systems incorporating significant computing and processing capability can achieve in such applications. The proposed system relies on a microcontroller unit and a camera which can take pictures of a double-sided sticky insect trap which, with the help of novel machine learning algorithms, can report on the presence of BMSB via a long-range communication link. The Internet of Things data capture and analysis system has recently been deployed in a real orchard in Italy which is subject to BMSB infestation and the first images have been analysed. This paper presents how the system works, the image processing, detection and classification algorithms, as well as a demonstration of the memory and energy consumption associated with the processing algorithms. The system achieves an accuracy of over 90% with multiple times less memory and energy consumption compared to other similar approaches in the literature.

关键词： Halyomorpha halys Edge computing Machine learning deep learning

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Intelligent detection method for aluminum alloy TIG welding quality by fusing multimodal data features

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PATTERN RECOGNITION LETTERS 2025年 189卷 106-114页

作者： Jiang, Pei Li, Xiaobin Huang, Wenming Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China

The welding process of aluminum alloys involves various parameters and rapid operations, resulting in complexity in the precise monitoring and control of the processing. Conventional real-time monitoring methods focus on identifying welding defects by acquiring operation data from a single sensor during the welding processing. However, the welding process frequently undergoes minor yet critical quality variations due to multiple external disturbances which cannot be detected by these traditional approaches, making it impossible to establish more accurate implicit correlations that fuse the diverse data. In this paper, a Resnet-Transformer model based on multimodal data fusion is proposed to efficiently identify six welding states in Tungsten Inert Gas Welding (TIG) by combining the features of welding molten pool image, welding current and welding speed. This model incorporates the benefits of Resnet and the Transformer encoder to fuse the features of images and time-series data using the Multi-modal Factorized Bilinear (MFB) method. Through experimental studies, the proposed model achieves a classification accuracy of 98.94 %. Furthermore, to improve the processing efficiency of the model, this paper presents an automatic segmentation and cropping enhancement algorithm for welding pool images, which removes the redundant features in the original image so as to increase the accuracy by 8.6 %.

关键词： TIG welding Multimodal data fusion deep learning Quality monitoring

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A comprehensive survey on image captioning: from handcrafted to deep learning-based techniques, a taxonomy and open research issues

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第11期56卷 13619-13661页

作者： Sharma, Himanshu Padha, Devanand Cent Univ Jammu Dept Comp Sci & Informat Technol Jammu & Kashmir Jammu 181124 India

image captioning is a pretty modern area of the convergence of computer vision and natural language processing and is widely used in a range of applications such as multi-modal search, robotics, security, remote sensing, medical, and visual aid. The image captioning techniques have witnessed a paradigm shift from classical machine-learning-based approaches to the most contemporary deep learning-based techniques. We present an in-depth investigation of image captioning methodologies in this survey using our proposed taxonomy. Furthermore, the study investigates several eras of image captioning advancements, including template-based, retrieval-based, and encoder-decoder-based models. We also explore captioning in languages other than English. A thorough investigation of benchmark image captioning datasets and assessment measures is also discussed. The effectiveness of real-time image captioning is a severe barrier that prevents its use in sensitive applications such as visual aid, security, and medicine. Another observation from our research is the scarcity of personalized domain datasets that limits its adoption into more advanced issues. Despite influential contributions from several academics, further efforts are required to construct substantially robust and reliable image captioning models.

关键词： Attention-based image captioning Encoder-decoder architecture image captioning Multimodal embedding

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deep learning-Driven Surveillance for Intrusion Identification using Proactive Intelligent Video Recognition 20

Deep Learning-Driven Surveillance for Intrusion Identificati...

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20th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)

作者： Challagundla, Yagnesh Parvatham, Shreeya Dheera Mohanty, Sachi Nandan Ravindra, J. V. R. VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati India Vardhaman Coll Engn Dept Elect & Commun Engn Hyderabad Telangana India

ISBN: (纸本)9798350374292;9798350374285

In today's security-conscious environment, the need for effective real-time weapon detection systems is paramount, especially in public spaces and sensitive areas. The primary objective of this research is to create a real-time system for detecting weapons utilizing advanced deep learning techniques, namely VGG16 and Faster RCNN. The system's objective is to precisely detect and categorize weapons from photos and video data, offering prompt notifications and improving security procedures. The project's backdrop explores the difficulties encountered, including data gathering, precision standards, immediate processing, confidentiality, and deployment considerations. The system achieves excellent accuracy and real-time detection capabilities by building a comprehensive dataset manually and training the models using GPUs and modern technologies. Using Python as the programming language provides flexibility and simplicity in development, making use of Python's modules such as OpenCV for image processing and Keras for deep learning models. The Tkinter framework enables the creation of a graphical user interface (GUI) that supports various user operations, such as uploading datasets, generating models, processing images and videos, detecting weapons, and visualizing results. The methodology employs a systematic approach, encompassing stages such as data preprocessing, model building, training, testing, and result analysis. The combination of VGG16 and Faster RCNN algorithms demonstrates a compromise between speed and accuracy, with Faster RCNN exhibiting greater performance in real-time detection. The project aims to achieve several objectives, including the compilation of a dataset, training a model, evaluating accuracy, implementing real-time detection, and establishing alerting methods. The system's range encompasses a wide range of surroundings, lighting situations, and orientations, allowing it to be flexible and suitable for a variety of security applications. To sum

关键词： real-time weapon detection deep learning algorithms VGG16 Faster RCNN Speed vs accuracy tradeoff real-time detection Performance analysis

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deep learning Based Fruit Harvest and Quality Control System (Agripick)

Deep Learning Based Fruit Harvest and Quality Control System...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Devi, R. Lakshmi Sangeetha, K. Saravanan, G. Bhavani, T. Kiruthika, Y. Madhumitha, N. Srivarshini, K.S. Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9798331510022

In recent years, the integration of deep learning technologies in agriculture has shown significant potential for enhancing efficiency and productivity. This paper presents a novel fruit picking and quality analysis system that leverages deep learning techniques to automate the harvesting process and assess fruit quality in real-time. Utilizing a combination of image processing and deep learning, the system analyzes visual attributes such as color, texture, and shape to evaluate fruit quality. The proposed solution not only reduces labor costs and minimizes waste but also ensures that only high-quality produce reaches consumers. Experimental results demonstrate high accuracy rates in fruit detection and quality assessment, showcasing the effectiveness of deep learning in agricultural applications. This system represents a significant advancement toward smart farming, contributing to sustainable agricultural practices and improved supply chain efficiency. © 2024 IEEE.

关键词： Automated Harvesting deep learning Fruit Detection Fruit Picking image processing Quality Analysis real-time Assessment

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An improved deep transfer learning approach to identify the human face mask in real-time considering the COVID-19 pandemic

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第7期83卷 21695-21743页

作者： Rusia, Mayank Kumar Singh, Dushyant Kumar Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Prayagraj Uttar Pradesh India

Face recognition systems have always been in great demand for various security-based applications, authentication being one of them. Due to the continuous upsurge of the COVID-19 pandemic and emerging variants of coronaviruses, wearing a face mask has been made mandatory in many countries, especially in crowded places. This situation poses significant challenges to the face recognition systems in recognizing the person's identity with face mask-based partial occlusion. Therefore, an update is needed in the traditional face recognition systems to ascertain whether the person is wearing a mask. This manuscript offers a novel Fiducial Point-based Non-local Means De-Noising (FP-NMDN) method for data pre-processing. This manuscript also proposed two comprehensive feature extraction mechanisms, i.e., transfer learning-based models and a customized Convolutional Neural Network (CNN) model. The experiment is conducted for five popular baseline architectures viz. Visual Geometry Group (VGG16), Residual Network (ResNet50), MobileNetV2, InceptionV3, and EfficientNetB0 with fine-tuning of hyperparameters and a customized CNN architecture. A modified dense network with a new classification layer has been introduced to obtain high classification results in less inference time. The datasets are collected from four valid sources;Kaggle Medical Masked Face, real-world Masked Face, Face Mask, and open-source datasets that have been resynthesized based on predefined experimental criteria named Dataset-I and other existing datasets as Dataset-II. The experimental results reveal that our optimized transfer learning-based ResNet50 model achieves the best accuracy of 99.68% and 99.67% for Dataset-I and Dataset-II, respectively. Besides, our customized CNN model outperforms other recent methods regarding overhead and inference time.

关键词： COVID-19 Partial occlusion Biometrics Computer vision Transfer learning image processing

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Faster RCNN based robust vehicle detection algorithm for identifying and classifying vehicles

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JOURNAL OF real-time image processing 2023年第5期20卷 93页

作者： Alam, Md Khorshed Ahmed, Asif Salih, Rania Al Asmari, Abdullah Faiz Saeed Khan, Mohammad Arsalan Mustafa, Noman Mursaleen, Mohammad Islam, Saiful Tongji Univ Sch Automot Studies Shanghai Peoples R China Tongji Univ Coll Civil Engn Dept Geotech Engn 1239 Siping Rd Shanghai 200092 Peoples R China Red Sea Univ Dept Civil Engn Port Sudan Sudan King Khalid Univ Coll Engn Civil Engn Dept Abha 61421 Saudi Arabia Univ Kiel Geomech & Geotech Grp D-24118 Kiel Germany Aligarh Muslim Univ ZH Coll Engn & Technol Dept Civil Engn Aligarh Uttar Pradesh India Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn 800 Dong Chuan Rd Shanghai 200240 Peoples R China China Med Univ Taiwan China Med Univ Hosp Taichung 40402 Taiwan

deep convolutional neural networks (CNNs) have shown tremendous success in the detection of objects and vehicles in recent years. However, when using CNNs to identify real-time vehicle detection in a moving context remains difficult. Many obscured and truncated cars, as well as huge vehicle scale fluctuations in traffic photos, provide these issues. To improve the performance of detection findings, we used multiscale feature maps from CNN or input pictures with numerous resolutions to adapt the base network to match different scales. This research presents an enhanced framework depending on Faster R-CNN for rapid vehicle recognition which presents better accuracy and fast processing time. Research results on our custom dataset indicate that our recommended methodology performed better in terms of detection efficiency and processing time, especially in comparison to the earlier age of Faster R-CNN models.

关键词： Classification deep learning Modified vgg16 Vehicle detection

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Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using image processing Template Matching

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SENSORS 2024年第11期24卷 3565页

作者： Abubakar, Aliyu Zachariades, Christos Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England

This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications.

关键词： condition monitoring electrical insulation image processing incipient fault insulation defect pattern recognition partial discharge PRPD template matching

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