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Adaptive Video Streaming in Multi-Tier Computing Networks: Joint Edge Transcoding and Client Enhancement

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2657-2670页

作者： Wang, Shuoyao Yang, Junyan Bi, Suzhi Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Hong Kong Joint Lab Big Data Imagingand Shenzhen 518060 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Peoples R China

With the advancement of multimedia technology and wireless networks, there is a growing demand for high-quality video streaming. Delivering stable video streaming in extremely dynamic wireless networks, nevertheless, is still an open problem. Recent developments in client computing and mobile edge computing (MEC) technologies have both shown promise in enhancing the adaptive bitrate (ABR) streaming services. In this paper, we consider a video streaming system in multi-tier computing networks, enabled by joint edge-side video transcoding and client-side video enhancement. By "enhancement," we mean that the client improves the video chunk quality via client-side image processing modules. In particular, we aim to design a joint bitrate adaptation, edge transcoding, and client image-processing algorithm, maximizing the quality of experience (QoE) of streaming services. The majority of the prior art has concentrated on super-resolution-enabled video streaming. Contrarily, we show that the video enhancement method outperforms the super-resolution approach in terms of signal-to-noise ratio and frames per second, implying a superior alternative for client-side processing in ABR streaming. We formulate the problem as an event-triggered Markov decision process (E-MDP), and propose a deep reinforcement learning (DRL)-based framework, named EDTEA. To deal with the delayed feedback induced by multi-tier computing, the entropy and the expected re-buffering terms are introduced to the objective and the reward, respectively. Extensive simulations based on real-world videos and bandwidth traces manifest that compared with state-of-the-art approaches, EDTEA provides 10.4%similar to 78.4% extra QoE while reducing re-buffering time by 85.5%similar to 91.7%.

关键词： Adaptive video streaming deep reinforcement learning edge computing video enhancement video transcoding

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Face Mask Detection Using Machine learning

Face Mask Detection Using Machine Learning

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Conference on real-time image processing and deep learning

作者： Eladham, Mohamed Nassif, Ali Bou AlShabi, Mohammad A. Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates Univ Sharjah Dept Mech & Nucl Engn Sharjah U Arab Emirates

ISBN: (数字)9781510661714

ISBN: (纸本)9781510661707;9781510661714

The COVID-19 epidemic forced governments to adopt worldwide lockdowns in order to limit the virus's spread. Wearing a face mask, it is said, would reduce the possibility of transmission. Due to the growing urban population, proper city management is more important than ever in the modern day to reduce the impacts of COVID-19 infection. To check the mask in public places, however, would require incredibly long lineups and delays. Therefore, it is necessary for an autonomous mask detection system to assess whether someone is wearing a face mask. On the face mask dataset, three different machine learning methods are applied to determine the likelihood of wearing a face mask. The models were assessed using a number of measures, including accuracy, recall, and ROC curve. The main objective of the study is to detect the presence of face masks using deep learning, machine learning, and image processing approaches. All three models-NB, KNN, and CNN-achieved noteworthy accuracy of more than 80%, with CNN showing the best overall performance.

关键词： covid-19 face mask detection machine learning deep learning

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Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection

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APPLIED INTELLIGENCE 2023年第12期53卷 15603-15620页

作者： Karaman, Ahmet Karaboga, Dervis Pacal, Ishak Akay, Bahriye Basturk, Alper Nalbantoglu, Ufuk Coskun, Seymanur Sahin, Omur Acibadem Hosp Dept Gastroenterol Kayseri Turkey Erciyes Univ Engn Fac Dept Comp Engn Kayseri Turkey Erciyes Univ Artificial Intelligence & Big Data Applicat & Res Kayseri Turkey Igdir Univ Engn Fac Dept Comp Engn Igdir Turkey

Colorectal cancer (CRC) is one of the most common and malignant types of cancer worldwide. Colonoscopy, considered the gold standard for CRC screening, allows immediate removal of polyps, which are precursors to CRC. Many computer-aided diagnosis systems (CADs) have been proposed for automatic polyp detection. Most of these systems are based on traditional machine learning algorithms and their generalization ability, sensitivity and specificity are limited. On the other hand, with the widespread use of deep learning algorithms in medical image analysis and the successful results in the analysis of colonoscopy images, especially in the early and accurate detection of polyps, these problems are eliminated in recent years. In short, deep learning algorithms and applications have gained a critical role in CAD systems for real-time autonomous polyp detection. Here, we make significant improvements to object detection algorithms to improve the performance of CAD-based real-time polyp detection systems. We integrate the artificial bee colony algorithm (ABC) into the YOLO algorithm to optimize the hyper-parameters of YOLO-based algorithms. The proposed method can be easily integrated into all YOLO algorithms such as YOLOv3, YOLOv4, Scaled-YOLOv4, YOLOv5, YOLOR and YOLOv7. The proposed method improves the performance of the Scaled-YOLOv4 algorithm with an average of more than 3% increase in mAP and a more than 2% improvement in F1 value. In addition, the most comprehensive study is conducted by evaluating the performance of all existing models in the Scaled-YOLOv4 algorithm (YOLOv4s, YOLOv4m, YOLOV4-CSP, YOLOv4-P5, YOLOV4-P6 and YOLOv4-P7) on the novel SUN and PICCOLO polyp datasets. The proposed method is the first study for the optimization of YOLO-based algorithms in the literature and makes a significant contribution to the detection accuracy.

关键词： Colorectal cancer deep learning Hyper-parameter optimization Artificial bee colony real-time polyp detection YOLOv4

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YOLO-ME: an enhanced lightweight YOLOv7 Tiny model for efficient object detection in aerial imagery

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SIGNAL image AND VIDEO processing 2025年第4期19卷 1-12页

作者： Junos, Mohamad Haniff Zulkifli, Safiah Bakar, Elmi Abu Hawary, Ahmad Faizul Khairuddin, Anis Salwa Mohd Univ Sains Malaysia Sch Aerosp Engn Engn Campus Nibong Tebal 14300 Penang Malaysia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur Malaysia

Vehicle detection in aerial images presents several challenges, including small object sizes, complex backgrounds, varied orientations, and scale variability, all influenced by changing illumination, weather conditions, and high image resolution. Current one-stage detection models have achieved excellent accuracy;however, their complex structures are unsuitable for real-time aerial object detection on embedded devices. To address this issue, this paper proposes the YOLO-ME model, developed based on the YOLOv7 Tiny architecture. The proposed model incorporates three major modifications, including adopting a novel SEELAN module in the backbone, the MELAN module in the neck section, and the adoption of Swish activation functions in most convolutional layers. Experimental results evaluated on the VisDrone dataset show that the YOLO-ME model surpasses other lightweight YOLO-based models, attaining an mAP of 24.82%. Additionally, it features the smallest model size and BFLOPs value at 18.8 MB and 4.68 BFLOPs, respectively. The proposed model demonstrates a detection speed comparable to the YOLOv7 Tiny model. The results indicate that the YOLO-ME model has great potential for real-time aerial object detection on embedded systems. Our code is available at: https://***/hanifjunos/YOLO-ME.

关键词： Aerial object detection deep learning YOLO Modified ELANS

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Predicting Pedestrian Crossing Intentions in Adverse Weather With Self-Attention Models

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 3250-3261页

作者： Elgazwy, Ahmed Elgazzar, Khalid Khamis, Alaa Ontario Tech Univ IoT Res Lab Oshawa ON L1G 0C5 Canada Ontario Tech Univ Fac Engn & Appl Sci Software Engn Oshawa ON L1G 0C5 Canada Canadian Univ Dubai Dubai U Arab Emirates King Fahd Univ Petr & Minerals IRC Smart Mobil & Logist Dhahran 31261 Saudi Arabia

The enhancement of the vehicle perception model represents a crucial undertaking in the successful integration of assisted and automated vehicle driving. By enhancing the perceptual capabilities of the model to accurately anticipate the actions of vulnerable road users, the overall driving experience can be significantly improved, ensuring higher levels of safety. Existing research efforts focusing on the prediction of pedestrians' crossing intentions have predominantly relied on vision-based deep learning models. However, these models continue to exhibit shortcomings in terms of robustness when faced with adverse weather conditions and domain adaptation challenges. Furthermore, little attention has been given to evaluating the real-time performance of these models. To address these aforementioned limitations, this study introduces an innovative framework for pedestrian crossing intention prediction. The framework incorporates an image enhancement pipeline, which enables the detection and rectification of various defects that may arise during unfavorable weather conditions. Subsequently, a transformer-based network, featuring a self-attention mechanism, is employed to predict the crossing intentions of target pedestrians. This augmentation enhances the model's resilience and accuracy in classification tasks. Through evaluation on the Joint Attention in Autonomous Driving (JAAD) dataset, our framework attains state-of-the-art performance while maintaining a notably low inference time. Moreover, a deployment environment is established to assess the real-time performance of the model. The results of this evaluation demonstrate that our approach exhibits the shortest model inference time and the lowest end-to-end prediction time, accounting for the processing duration of the selected inputs.

关键词： Pedestrians Feature extraction Visualization Predictive models Transformers Meteorology Accuracy image enhancement Videos Odometry Pedestrian intention image enhancement assisted and automated driving vehicles

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Multi-Head multimodal deep interest recommendation network

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KNOWLEDGE-BASED SYSTEMS 2023年第1期276卷

作者： Yang, Mingbao Zhou, Peng Li, Shaobo Zhang, Yuanmeng Hu, Jianjun Zhang, Ansi Guizhou Univ Coll Comp Sci & Technol State Key Lab Pub Big Data Guiyang Peoples R China Guizhou Univ Sch Mech Engn Guiyang Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC USA Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China

From machine learning recommendation to deep learning recommendation, reinforcement learning recommendation, and recommendation model compression, the network structure of the recommen-dation algorithm becomes more complex, more expressive, and more lightweight with higher real-time performance. However, most of these models are dedicated to recommendations by mining ratings and text information, and researchers have optimized the network structures of many recommendation models. However, mining image information and enriching recommendation model features are less studied, and there is still some room for optimization of deep recommendation model structures and real-time performance. To this end, this paper proposes a model called MMDIN by adding multi-head and multi-modal modules to the DIN model. The multi-modal module extracts image features, which enriches the feature set that the model can use and strengthens the cross-combination and fitting expression capabilities of the model. Meanwhile, the multi-head mechanism extracts features from different dimensions, which further enhances the model prediction performance. Experimental results show that the proposed MMDIN model improves the recommendation prediction performance, and it outperforms state-of-the-art methods in comprehensive indicators. & COPY;2023 Published by Elsevier B.V.

关键词： Multimodal Multi-Head deep learning recommendation deep interest recommendation network Attention-based recommender system

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Parallel Architecture for Low Latency UAV Detection and Tracking Using Robotic Telescopes

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 5515-5524页

作者： Ojdanic, Denis Naverschnigg, Christopher Sinn, Andreas Zelinskyi, Daniil Schitter, Georg TU Wien Automat & Control Inst ACIN A-1040 Vienna Austria

This article presents the implementation of a multithreaded parallel architecture, which enables telescope-based optical unmanned aerial vehicle (UAV) detection and tracking in real time. For efficient image processing an accurate deep learning object detector is complemented in parallel by a fast object tracker. A transition strategy between detector and tracker is introduced based on the tracker reliability, which improves the object localization accuracy of the system. The deep learning algorithm initializes the tracker and in the subsequent frames the reliability of the tracker is compared to the confidence value of each newly detected object to determine whether a reinitialization is necessary. The implemented architecture successfully demonstrates the parallel combination of an FRCNN detector and a MEDIANFLOW tracker to achieve visual UAV detection and tracking at 100 fps. The proposed reliability-based strategy outperforms a purely detector and tracker-based strategy by 6% and 14%, respectively, in terms of intersection over union at a threshold of 0.5, in scenarios, when the target UAV is flying in front of a complex background. In addition, the implemented parallel architecture increases the probability for a flight path estimation, which requires at least two localizations, by 49%, when compared to a nonparallel architecture. Field tests are conducted with the proposed architecture using a telescope system demonstrating UAV detection and tracking at 100 fps in distances up to 4000 m in front of a clear background.

关键词： Detectors Autonomous aerial vehicles Reliability deep learning Radar tracking Telescopes Cameras detection parallel tracking unmanned aerial vehicle (UAV)

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Online penetration prediction based on multimodal continuous signals fusion of CMT for full penetration

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JOURNAL OF MANUFACTURING PROCESSES 2024年 115卷 431-440页

作者： Gao, Peng Su, Xiaocong Wu, Zijian Lu, Jun Han, Jing Bai, Lianfa Zhao, Zhuang Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Sen Nanjing 210094 Peoples R China

Online penetration monitoring for complex butt welding is challenging due to steel plate's groove instability and welding heat deformation. In this paper, automatic cold metal transfer (CMT) welding is used to join two complex bevelled austenitic stainless steel with SS304 as the base metal. This work reports a hybrid approach combining deep learning, computer vision, and sound signal processing to monitor groove welding penetration under full penetration in real time. Sequence signals such as video and sound can complimentarily characterize the melt pool state. In this paper, the proposed Multimodal continuous signals Characteristic Reinforcement Network (MCRNet) utilizes 3D convolution and multiscale convolution with channel attention to considerably improve the performance of lightweight networks. At the same time, a new fusion method with similarity loss is proposed to cope with the input of visual and acoustic signals. That improves the effect by at least 18 % compared with the single-modal signal input. The experimental results show that the Mean Square Error (MSE) of MCRNet improved the performance by 44 % compared with the mainstream deep learning framework. Meanwhile, the inference speed under multimodal input reaches 57 frames per second (FPS). MCRNet finally achieves online penetration accurate prediction of the melt pool.

关键词： Audio-visual signal Penetration state deep learning

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Fine-tuned depth-augmented U-Net for enhanced semantic segmentation in indoor autonomous vision systems

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JOURNAL OF real-time image processing 2025年第1期22卷 1-12页

作者： Tran, Hoang N. Le, Thu A. N. Nguyen, Nghi V. Nguyen, Nguyen T. Nguyen, Anh D. FPT Univ Can Tho 94000 Vietnam Vietnam Natl Univ Ho Chi Minh City Univ Technol HCMUT Fac Mech Engn Dept Mechatron Engn Ho Chi Minh City Vietnam

Recent technological advancements have significantly improved indoor autonomous vision systems (IAVSs), underscoring the critical need to enhance their capability to interpret real-world environments in a manner similar to human perception. In response to this challenge, this paper introduces DEADFL-UNet, a groundbreaking framework that enhances the existing EADFL-UNet architecture. EADFL-UNet utilized the EfficientNetB3 model, supplemented by a new Super Attention Block and CBW-FL Loss Function, to tackle the significant data imbalance found in the NYUv2 dataset. Our enhancement focuses on using the MobileNetV2 model in conjunction with several fine-tuning techniques to maximize Depth characteristics in tandem with RGB ones inside the prior architecture. By applying the proposed techniques, we achieved an improvement of approximately 6% in mIoU (Mean Intersection over Union) compared to the original EADFL-UNet model, which was previously published. Furthermore, the difference between the fine-tuned and non-fine-tuned versions is 1.91% in mIoU, demonstrating the significant effectiveness of the fine-tuning technique. To confirm the real-time FPS (Frame Per Second) performance of each model, this technique has undergone extensive testing and assessment using standard metrics, not only on pre-existing datasets but also in a ROS2 (Robot Operating System) simulation environment. These proven techniques have potential for various applications in autonomous systems, such as robotic vision, GPS (Global Positioning System) position tracking, autonomous vehicles, and security, improving accuracy and efficiency.

关键词： deep learning Autonomous systems (AS) Segmentation EADFL-UNet EfficientNet MobileNet Fine-tuning Scene detection

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Steel surface defect detection based on the lightweight improved RT-DETR algorithm

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JOURNAL OF real-time image processing 2025年第1期22卷 1-15页

作者： Mao, Haojie Gong, Yongwang Yancheng Inst Technol Sch Informat Engn Yancheng 224051 Jiangsu Peoples R China

The existing object detection algorithms for steel surface defects detection have several drawbacks such as large model size, low efficiency, more parameters, and slow speed which limit their application in production. To solve these problems, in this paper a lightweight improved object detection algorithm, named LRT-DETR, is proposed based on the RT-DETR algorithm. Firstly, the BasicBlock modules in RT-DETR are replaced by the lightweight MobileNetV3 module. As a feature extraction backbone, it can fully capture the long-range feature interactions of steel defects and the local feature interactions. Secondly, a more efficient depthwise separable convolution (DWConv) and the VoVGSCSP structure are introduced into RT-DETR to refine the feature fusion network, which thereby enhances the feature extraction and fusion, as well as reduces the computational complexity of the algorithm and achieves a lightweight effect. Finally, a novel bounding box similarity measure MPDIoU is used to replace the loss function in the original network providing a more accurate measure of the similarity between bounding boxes to improve the accuracy of the model. Experimental results show that an average precision (AP) of the proposed LRT-DETR algorithm is 74.8%, 1.5% higher than the original algorithm, and the number of parameters and computational cost are reduced by 65.90% and 75.96%, respectively. Further experimental results demonstrate that the LRT-DETR algorithm is better than other compared algorithms in terms of detection speed and accuracy. Our algorithm can be used in the real-time and efficient detection of steel surface defects.

关键词： deep learning Lightweighting Defect detection Neural network Loss function

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