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Research On Fatigue Driving Detection Based On Deep Learning

engineering Letters 2025年第2期33卷 348-356页

作者： Zhou, Huinan Dai, Hong University of Science and Technology Liaoning Liaoning AnshanCO 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning AnshanCO 114051 China

Fatigue driving is one of the main causes of traffic accidents. Effective fatigue driving detection technology can reduce traffic accidents caused by fatigue driving. Traditional fatigue driving detection methods usually use a two-stage detection method, which has the problems of low efficiency and poor accuracy. To solve these problems, this paper designs a single-stage fatigue driving detection model based on separable convolution Road Safety Net (RSNet), which extracts the features in the driver's image or video and classifies these features to determine whether the driver is in an abnormal driving state. RSNet borrows the structure of Swin Transformer and adopts a hierarchical design, which makes the model better able to extract features and classify. Each level is composed of a GDS Layer and multiple RS Blocks. To solve the problem of low efficiency of traditional methods, we use separable convolution in RS Block to replace the traditional convolutional neural network, which reduces the complexity of the model and effectively improves the computational efficiency of the model. Experimental results show that the Top-1 and Top-5 Accuracy of RSNet on the HMDB51 dataset reaches 85 % and 97.35 %, respectively, and the delay is 5.97 ms. Compared with ResNet, EfficientNet, and Swin Transformer, RSNet has higher accuracy and efficiency. In addition, this paper takes the YOLACT algorithm as an example, selects different backbone networks for object detection, and compares the indicators of mean Average Precision (mAP) and Frames Per Second (FPS). Experiments show that RSNet performs better when used as the backbone network for object detection, with mAP reaching 32.4 % and FPS of 39.6 ms. It indicates that RSNet performs well in action recognition tasks. © 2025, International Association of Engineers. All rights reserved.

关键词： Convolutional neural networks

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Scalable Motion Estimation and Temporal Context Reinforcement for Video Compression Using RGB Sensors

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IEEE Sensors Journal 2025年第10期25卷 18323-18333页

作者： Huang, Qian Lu, Hao Liu, Wenting Wang, Yiming Hohai University College of Computer Science and Software Engineering Nanjing211100 China Jiangsu Engineering Research Center of Digital Twinning Technology for Key Equipment in Petrochemical Process Changzhou213164 China

We propose a novel deep learned video compression technique, named scalable motion estimation (SME), which is designed for video data generated by sensor systems in smart devices. These devices face unique challenges due to limited bandwidth, constrained computational resources, and the complexity of real-world scenarios. Existing learned video compression methods primarily rely on optical flow for motion estimation;however, optical flow often suffers from inaccuracies and displacement errors, resulting in imprecise motion vectors, and the increasing complexity of models further limits their deployment in resource-constrained environments. To address these issues, the SME framework introduces a hierarchical motion vector enhancement strategy that progressively optimizes motion information in video captured by sensors. In addition, we propose the temporal context reinforcement (TCR) module, which partitions high-dimensional contextual information into three components. Compared to direct context encoding methods, the TCR module optimizes bit allocation, achieving a more balanced distribution of high- and low-frequency information. Extensive experiments on benchmark datasets demonstrate that the SME framework substantially enhances the efficiency and performance of video compression systems on RGB sensor-captured videos, making it particularly suitable for video applications. © 2001-2012 IEEE.

关键词： Optical flows

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Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability

Reliability of deep learning models for anatomical landmark ...

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Medical Imaging 2025: Image Processing

作者： Salari, Soorena Rivaz, Hassan Xiao, Yiming Computer Science and Software Engineering Concordia University Montreal Canada Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (纸本)9781510685901

Automated detection of anatomical landmarks plays a crucial role in many diagnostic and surgical applications. Progresses in deep learning (DL) methods have resulted in significant performance enhancement in tasks related to anatomical landmark detection. While current research focuses on accurately localizing these landmarks in medical scans, the importance of inter-rater annotation variability in building DL models is often overlooked. Understanding how inter-rater variability impacts the performance and reliability of the resulting DL algorithms, which are crucial for clinical deployment, can inform the improvement of training data construction and boost DL models' outcomes. In this paper, we conducted a thorough study of different annotation-fusion strategies to preserve inter-rater variability in DL models for anatomical landmark detection, aiming to boost the performance and reliability of the resulting algorithms. Additionally, we explored the characteristics and reliability of four metrics, including a novel Weighted Coordinate Variance metric to quantify landmark detection uncertainty/inter-rater variability. Our research highlights the crucial connection between inter-rater variability, DL-models performances, and uncertainty, revealing how different approaches for multi-rater landmark annotation fusion can influence these factors. © 2025 SPIE.

关键词： Deep learning

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Federated Deep Reinforcement Learning for Multimodal Content Caching in Edge-Cloud Networks

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IEEE Transactions on Network science and engineering 2025年第3期12卷 2188-2201页

作者： Feng, Weijia Zuo, Xinyu Zhang, Ruojia Zhu, Yichen Wang, Chenyang Guo, Jia Sun, Chuan Tianjin Normal University School of Computer and Information Engineering Tianjin300387 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518132 China Shenzhen University Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen518132 China Chongqing Normal University School of Computer and Information Science Chongqing401333 China

Edge caching presents a promising avenue for mitigating backbone network congestion by strategically caching frequently accessed content at the network periphery. As most current edge caching solutions are designed for single-modal content requests, they cannot deal with the increasing volume of multi-modal content requests. In this study, we investigate the issue of multimodal content caching in edge-cloud networks. Firstly, we establish a heterogeneous edge-cloud network adept at caching multimodal content proximate to end-users to facilitate expeditious content delivery. By leveraging latent representations of multimodal content, we identify distinct user request modalities for multimodal content. Subsequently, we formulate caching replacement operations as a Markov Decision Process (MDP) aimed at minimizing user-content access latency. Moreover, we propose a decentralized multimodal content caching framework at the network edge based on federated deep reinforcement learning. This framework affords distributed decision-making and learning capabilities, thereby alleviating the strain on centralized resources and augmenting caching efficacy. To demonstrate the efficacy of our proposed framework, we conduct comprehensive experiments utilizing the Noah-Wukong dataset. Experimental results provide evidence that our framework reduces average latency by up to 10% compared to traditional methods, highlighting its proficiency in enhancing cache performance in edge-cloud networks. © 2013 IEEE.

关键词： Federated learning

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Blockchain-enabled IoT access control model for sharing electronic healthcare data

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Multimedia Tools and Applications 2025年第10期84卷 8127-8148页

作者： Ilyas, Benkhaddra Kumar, Abhishek Ali, Setitra Mohamed Lei, Hang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Chandigarh University Gharuan Mohali Punjab140413 India School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

Deploying the Internet of Things (IoT) in the transfer of enormous medical data often promotes challenges with the security, confidentiality, and privacy of the user’s sensitive data. In addition, the access control for prohibiting unauthorized access has been facing challenges with single-point-of-failure, collusion, and other security issues. To address these challenges associated with IoT access control, this research proposes a Blockchain-enabled IoT access control for sharing Electronic health records (EHR). The proposed authentic access control scheme transfers the data to the authenticated user without discrepancies. The proposed technique utilizes the hyperledger composer fabric blockchain that improves the transparency and traceability of the network, and the intelligent contract keeps track of the information, which reduces the possibility of unauthorized access. The proposed access control scheme validates the request effectively and transmits the data efficiently. The effectiveness of the proposed access control scheme is evaluated by analyzing the blocks in terms of metrics such as responsiveness of 185.47 secs, 95% Genuine User Detection (GUD), 96.50% Privacy, and 3.50% Information loss, for 100 blocks and proposed access control scheme works more efficiently in transmitting the data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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CLS-3D: Content-Wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection in Autonomous Vehicles

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IEEE Access 2025年 13卷 69840-69856页

作者： Mushtaq, Husnain Latif, Sohaib Saad Bin Ilyas, Muhammad Mohsin, Syed Muhammad Ali, Mohammed Central South University School of Computer Science and Engineering Changsha410083 China Grand Asian University Department of Computer Science and Software Engineering Sialkot51040 Pakistan The University of Chenab Department of Computer Science Gujranwala50700 Pakistan COMSATS University Islamabad Department of Computer Science Islamabad45550 Pakistan Virtual University of Pakistan Standard College of Modern Sciences Lahore55150 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia Abha61421 Saudi Arabia

Accurate 3D object detection is vital in autonomous driving. Single-modal detectors, using either camera or LiDAR, struggle with issues like limited depth perception or difficulty in distinguishing semantically similar objects. While multimodal approaches aim to address these limitations by combining LiDAR and camera data, they often face complexities in integrating sparse and uneven point cloud distributions, resulting in inefficient feature fusion. To tackle these challenges, we propose CLS-3D (Content-wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection). This novel framework fuses LiDAR and camera features using a single multi-modal backbone and augments them with the semantic probabilities obtained from the image stream. Our method captures local and global spatial relationships through a slot reweighting mechanism and incorporates I3C-IoU loss for precise box regression. The semantically augmented features, via a single multi-modal backbone, are embedded using a content-based transformer and processed through a slot-wise auto-encoder structure with channel-wise positional embeddings and a feed-forward MLP network. Our model improves temporal consistency and detection accuracy by dynamically adjusting feature relevance through slot-wise reweighting. We further define a I3C-IoU metric, considering centre, overlap, and scale for enhanced box regression accuracy. This mechanism allows the model to focus on significant temporal information, enhancing its ability to learn complex sequences and improving the overall performance of 3D object detection, especially in challenging scenarios such as occlusion and long-range detection. Extensive experiments on the KITTI and nuScenes benchmark demonstrate that CLS-3D achieves state-of-the-art performance, with 89.52% 3D mAP and 94.08% BEV mAP, outperforming existing methods. © 2013 IEEE.

关键词： Depth perception

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. © 2025 IEEE.

关键词： Prompt Learning Vision-Language Models Zero-shot Anomaly Detection

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Identifying Performance Inefficiencies of Parallel Program with Spatial and Temporal Trace Analysis

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1387-1400页

作者： Xuan, Zhibo Sun, Xin You, Xin Yang, Hailong Luan, Zhongzhi Liu, Yi Qian, Depei Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing100191 China

Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constrains their practical adoption to identify performance inefficiencies. In this paper, we propose STAD, a performance analysis tool for parallel programs that considers both spatial and temporal patterns within trace data. STAD captures the spatial communication patterns between processes using a spatial communication pattern graph. It then adopts a dynamic graph neural network-based unsupervised model to learn the evolving temporal patterns along the timeline. Additionally, STAD diagnoses the root causes of performance anomalies by exploiting the aggregated feature of anomalies along the call tree. Our evaluation results demonstrate that STAD can effectively detect performance anomalies with acceptable overhead and diagnose the root causes attributed to both the program itself and the running environment. © 1990-2012 IEEE.

关键词： Performance Analysis Training Libraries Graph Neural Networks Analytical Models Accuracy Sun Phasor Measurement Units Heating Systems Hardware Anomalies Parallel Programs Performance Analysis Root Cause Parallel Computing Spatial Patterns Communication Patterns Trace Data Programming Tools Dynamic Graph Artificial Neural Network Matrix Elements Unsupervised Learning Unique Way Anomaly Detection Red Points Graph Convolutional Network System Noise Variational Autoencoder Graph Neural Networks Program Execution Time Slice Network Congestion Node Score Anomaly Score Dynamic Topology Graph Attention Network Dynamic Neural Network Load Imbalance White Blocks Dynamic Network Graph Neural Network Model Tree View Vector Of Node

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MA-MFIF: When misaligned multi-focus Image fusion meets deep homography estimation

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Multimedia Tools and Applications 2025年第12期84卷 10877-10898页

作者： Zhao, Baojun Luo, Fei Fuentes, Joel Ding, Weichao Gu, Chunhua School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Department of Computer Science and Information Technologies Universidad del Bio-Bio Chillán3780000 Chile

Multi-focus image fusion is a technique that combines multiple out-of-focus images to enhance the overall image quality. It has gained significant attention in recent years, thanks to the advancements in deep learning. However, one of the persistent challenges in this field is the processing of misaligned data, which can negatively impact the fusion results. To overcome this problem, a novel fusion framework with pre-registration is proposed for the fusion of misaligned multi-focus images. For pre-registration, content-aware deep homography estimation is used, which performs transfer learning on a real multi-focus image dataset to adapt to registration under defocused conditions. For fusion, a fusion module with dual-branch feature interaction is utilized to avoid invalid feature fusion and trained on real light field dataset to achieve better fusion performance. Qualitative and quantitative experimental results show that the proposed method has a 2-3 percentage point improvement in multiple evaluation metrics compared to existing advanced registration and fusion methods, and a maximum improvement of 4.83 percentage points in fusion performance when tested independently on the Lytro dataset. Additionally, We find that the value of the Qcv metric is greatly influenced by the alignment status of the input images, leading to its inability to reflect the fusion quality of aligned images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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An empirical study of manual abstraction between class diagrams and code of open-source systems

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software and Systems Modeling 2025年 1-27页

作者： Zhang, Wenli Zhang, Weixing Strüber, Daniel Hebig, Regina Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden Department of Software Science Radboud University Nijmegen Netherlands Institute of Computer Science University of Rostock Rostock Germany

Models play a crucial role in software design, analysis, and supporting new maintainers. However, over time, the benefits of models can diminish as system implementations evolve without corresponding updates to the original models. Reverse engineering methods and tools can help maintain alignment between models and implementation code. Yet, automatically reverse-engineered models often lack abstraction and contain extensive details that hinder comprehension. Recent advancements in AI-based content generation suggest that we may soon see reverse engineering tools capable of human-grade abstraction. To guide the design and validation of such tools, we need a principled understanding of manual abstraction—a topic that has received limited attention in existing literature. In pursuit of this goal, our paper presents a multiple-case study of model-to-code differences, examining nine substantial open-source software projects obtained through repository mining. We manually matched source code from projects comprising 4983 classes, 26k attributes, and 54k operations to 523 model elements (including classes, attributes, operations, and relationships). These mappings precisely capture discrepancies between provided class diagram designs and actual implementation code. By analyzing these differences in detail, we derive a taxonomy of difference types and provide a well-organized list of cases corresponding to identified differences. Our findings have the potential to contribute to improved reverse engineering methods and tools, propose new mapping rules for model-to-code consistency checks, and offer guidelines to avoid over-abstraction and over-specification during the design process. © The Author(s) 2025.

关键词： Abstraction Modeling software design

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