Journal of King Saud University-Computer and Information Sciences最新文献_第3页

A hierarchical and secure approach for automotive firmware upgrades 一种用于汽车固件升级的分层安全方法

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-28 DOI: 10.1016/j.jksuci.2024.102258

Feng Luo , Zhihao Li , Jiajia Wang , Cheng Luo , Hongqian Liu , Dengcheng Liu

With the development of intelligent and connected vehicles, the expansion of software necessitates an increased significance and frequency of automotive firmware upgrades. The abundance of potential attack vectors and valuable data renders these upgrades enticing targets for attackers. However, the prevailing security services used for automotive firmware upgrades are no longer sufficient to meet security requirements. Hence, this paper proposes a Secure Automotive Firmware Upgrade Approach (SAFUA), aimed at enhancing authentication and communication security during automotive firmware upgrades. To address the heterogeneous performance of in-vehicle nodes and diverse application contexts, this approach introduces multiple authentication modes tailored to various upgrade scenarios. Moreover, hierarchical authentication and secure communication strategies are designed to achieve a balance between security and efficiency requirements. Consolidating these methodologies, a standardized automotive firmware upgrade process is delineated. Formal and informal verification of the proposed approach is conducted to attest its security efficacy. Furthermore, a simulated vehicular environment is constructed to evaluate the temporal and spatial efficiency of the approach across diverse bus and device configurations. The results confirm the adaptability of the secure upgrade approach outlined herein to the automotive firmware upgrade landscape, offering robust security alongside enhanced upgrade efficiency.

随着智能网联汽车的发展，软件的扩展要求汽车固件升级的重要性和频率增加。丰富的潜在攻击向量和有价值的数据使这些升级成为攻击者的诱人目标。然而，目前用于汽车固件升级的安全服务已不足以满足安全需求。为此，本文提出了一种安全的汽车固件升级方法（SAFUA），旨在提高汽车固件升级过程中的身份验证和通信安全性。为了解决车载节点的异构性能和各种应用程序上下文的问题，该方法引入了针对各种升级场景量身定制的多种身份验证模式。此外，还设计了分层身份验证和安全通信策略，以实现安全性和效率需求之间的平衡。整合这些方法，描述了标准化的汽车固件升级过程。对所提出的方法进行了正式和非正式的验证，以证明其安全有效性。此外，还构建了一个模拟车辆环境来评估该方法在不同总线和设备配置下的时空效率。结果证实了本文概述的安全升级方法对汽车固件升级领域的适应性，在提高升级效率的同时提供强大的安全性。

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引用次数: 0

T-SRE: Transformer-based semantic Relation extraction for contextual paraphrased plagiarism detection T-SRE：基于转换的语义关系提取，用于上下文释义抄袭检测

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-26 DOI: 10.1016/j.jksuci.2024.102257

Pon Abisheka , C. Deisy , P. Sharmila

Plagiarism has become a pervasive issue in academics and professionals to safeguard academic integrity and intellectual property rights. The escalating sophistication of plagiarized content through semantic manipulation and structural reorganization poses significant challenges to existing detection systems that rely primarily on lexical similarity measures. The proposed T-SRE (Transformer-based Semantic Relation Extraction), a novel framework addresses the limitations of traditional n-gram and string-matching approaches by leveraging deep semantic analysis. The proposed framework combines Dependency Parsing (DP) for syntactic relationship mapping and Named Entity Recognition (NER) for contextual entity identification, augmented by a transformer-based neural network that captures long-range contextual dependencies. This learning methodology incorporates three key components: a position-aware word reordering algorithm, Levenshtein distance metric for structural similarity, and contextual word embeddings for semantic preservation detection. The proposed T-SRE enhances text structure recognition by combining position-aware reordering with semantic preservation through ensemble learning. The system implements a hierarchical classification scheme that quantifies plagiarism severity through a four-tier taxonomy: heavy, low, non-plagiarized and verbatim copy. The Udacity benchmark dataset showcases the model’s superior detection capabilities, achieving 92% precision, 89% recall, and an F1-score of 90.5%, particularly in lightweight textual modifications.The framework achieves a granularity score of 1.28, outperforming existing approaches.

为了维护学术诚信和知识产权，剽窃已成为学术界和专业人士普遍存在的问题。通过语义操纵和结构重组不断升级的剽窃内容复杂性对主要依赖词汇相似性度量的现有检测系统提出了重大挑战。本文提出的基于变换的语义关系提取（T-SRE）框架利用深度语义分析解决了传统n图和字符串匹配方法的局限性。该框架结合了用于句法关系映射的依赖解析（DP）和用于上下文实体识别的命名实体识别（NER），并通过基于转换器的神经网络进行增强，以捕获远程上下文依赖关系。该学习方法包含三个关键组件：位置感知词重排算法，用于结构相似性的Levenshtein距离度量，以及用于语义保存检测的上下文词嵌入。本文提出的T-SRE通过集成学习将位置感知重排序和语义保存相结合来增强文本结构识别。该系统实现了一种分层分类方案，通过四层分类来量化抄袭的严重程度：重抄袭、低抄袭、非抄袭和逐字抄袭。Udacity基准数据集展示了该模型卓越的检测能力，达到了92%的准确率、89%的召回率和90.5%的f1分数，特别是在轻量级文本修改方面。该框架的粒度得分为1.28，优于现有的方法。

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引用次数: 0

Audio analysis with convolutional neural networks and boosting algorithms tuned by metaheuristics for respiratory condition classification 基于卷积神经网络的音频分析和基于元启发式算法的呼吸条件分类

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-12-06 DOI: 10.1016/j.jksuci.2024.102261

Safet Purkovic , Luka Jovanovic , Miodrag Zivkovic , Milos Antonijevic , Edin Dolicanin , Eva Tuba , Milan Tuba , Nebojsa Bacanin , Petar Spalevic

In contemporary medical research, respiratory disorders have become a primary focus. Improving patient outcomes for any medical condition largely depends on early identification and prompt treatment. Traditionally, medical professionals diagnose respiratory diseases by auscultating a patient’s breathing. However, this method has inherent limitations, as it may not enable physicians to accurately identify every respiratory condition. This study explores the potential of using convolutional neural networks (CNNs) in conjunction with audio analysis for the identification of respiratory problems. This work proses a novel two-tier framework that integrates CNNs with extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) models to classify respiratory conditions. Additionally, modern optimization techniques are employed to enhance classification efficiency, recognizing the significant impact that appropriate hyperparameter tuning has on machine learning (ML) and deep learning (DL) performance. This research introduces a modified version of particle swarm optimization (PSO) tailored to meet the specific needs of ML and DL tuning. The proposed approach is validated using a real-world clinical dataset. Two studies, both based on mel spectrograms of patient breathing patterns, were conducted: the first aimed at determining whether patients have respiratory conditions (binary classification), while the second employed the same data structure for multi-class classification. In both scenarios, advanced optimizers were utilized to optimize model architecture and training settings. Under identical testing conditions, the proposed PSO metaheuristic achieved an accuracy of 98.14

%

for respiratory condition detection in binary classification and a slightly lower accuracy of 81.25

%

for specific condition identification in multi-class classification.

在当代医学研究中，呼吸系统疾病已成为一个主要的焦点。改善任何疾病患者的预后在很大程度上取决于早期发现和及时治疗。传统上，医学专业人员通过听诊病人的呼吸来诊断呼吸系统疾病。然而，这种方法有固有的局限性，因为它可能不能使医生准确地识别每一种呼吸系统疾病。本研究探讨了将卷积神经网络（cnn）与音频分析相结合用于识别呼吸问题的潜力。这项工作提出了一个新的两层框架，该框架将cnn与极端梯度增强（XGBoost）和自适应增强（AdaBoost）模型集成在一起，以对呼吸条件进行分类。此外，采用现代优化技术来提高分类效率，认识到适当的超参数调优对机器学习（ML）和深度学习（DL）性能的重要影响。本研究引入了一种改进版本的粒子群优化（PSO），以满足ML和DL调优的特定需求。所提出的方法使用现实世界的临床数据集进行了验证。进行了两项研究，均基于患者呼吸模式的mel谱图：第一项研究旨在确定患者是否患有呼吸疾病（二元分类），而第二项研究采用相同的数据结构进行多类分类。在这两种情况下，使用高级优化器来优化模型架构和训练设置。在相同的测试条件下，所提出的PSO元启发式方法在二元分类中呼吸状态检测的准确率为98.14%，在多类分类中特定状态识别的准确率略低，为81.25%。

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引用次数: 0

Enhanced secure lossless image steganography using invertible neural networks 利用可逆神经网络增强安全无损图像隐写

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-28 DOI: 10.1016/j.jksuci.2024.102259

Weida Chen , Weizhe Chen

Image steganography is a technique that embeds secret data into cover images in an imperceptible manner, ensuring that the original data can be recovered by the receiver without arousing suspicion. The key challenges currently faced by image steganography are capacity, invisibility, and security. We suggest an invertible neural network-based image steganography technique to concurrently address these three issues. To achieve better invisibility, we adopt a method that avoids the loss of information, thereby preventing ill-posed problems. The learning cost during image embedding can be reduced by only fitting part of the color channels in order to address the issue of high capacity. Additionally, we introduce the concept of a key to constrain the embedding process of the secret information, significantly enhancing the security of the hidden data. According to our experimental results, our method outperforms other image steganography algorithms on DIV2K, COCO, and ImageNet datasets, achieving perfect recovery of the secret images, its PSNR and SSIM can reach the theoretical maximum values.

图像隐写术是一种将秘密数据以不易察觉的方式嵌入封面图像的技术，它能确保接收者在不引起怀疑的情况下恢复原始数据。图像隐写术目前面临的主要挑战是容量、隐蔽性和安全性。我们提出了一种基于可逆神经网络的图像隐写技术，以同时解决这三个问题。为了达到更好的隐蔽性，我们采用了一种避免信息丢失的方法，从而避免了不合理的问题。为了解决高容量问题，我们只拟合了部分颜色通道，从而降低了图像嵌入过程中的学习成本。此外，我们还引入了密钥的概念来约束秘密信息的嵌入过程，从而大大提高了隐藏数据的安全性。根据实验结果，我们的方法在 DIV2K、COCO 和 ImageNet 数据集上的表现优于其他图像隐写算法，实现了秘密图像的完美恢复，其 PSNR 和 SSIM 均达到了理论最大值。

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引用次数: 0

Binocular camera-based visual localization with optimized keypoint selection and multi-epipolar constraints 通过优化关键点选择和多极性约束进行基于双目摄像头的视觉定位

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-05 DOI: 10.1016/j.jksuci.2024.102228

Guanyuan Feng, Yu Liu, Weili Shi, Yu Miao

In recent years, visual localization has gained significant attention as a key technology for indoor navigation due to its outstanding accuracy and low deployment costs. However, it still encounters two primary challenges: the requirement for multiple database images to match the query image and the potential degradation of localization precision resulting from the keypoints clustering and mismatches. In this research, a novel visual localization framework based on a binocular camera is proposed to estimate the absolute positions of the query camera. The framework integrates three core methods: the multi-epipolar constraints-based localization (MELoc) method, the Optimal keypoint selection (OKS) method, and a robust measurement method. MELoc constructs multiple geometric constraints to enable absolute position estimation with only a single database image, while OKS and the robust measurement method further enhance localization accuracy by refining the precision of these geometric constraints. Experimental results demonstrate that the proposed system consistently outperforms existing visual localization systems across various scene scales, database sampling intervals, and lighting conditions

近年来，视觉定位因其出色的精度和较低的部署成本成为室内导航的一项关键技术，受到广泛关注。然而，它仍然面临两个主要挑战：一是需要多个数据库图像来匹配查询图像，二是关键点聚类和不匹配可能导致定位精度下降。本研究提出了一种基于双目摄像头的新型视觉定位框架，用于估算查询摄像头的绝对位置。该框架集成了三种核心方法：基于多极约束的定位（MELoc）方法、最优关键点选择（OKS）方法和稳健测量方法。MELoc 构建了多个几何约束条件，只需一张数据库图像即可实现绝对位置估算，而 OKS 和稳健测量方法则通过完善这些几何约束条件的精度来进一步提高定位精度。实验结果表明，在不同的场景尺度、数据库采样间隔和照明条件下，所提出的系统始终优于现有的视觉定位系统。

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引用次数: 0

Anomaly detection in sensor data via encoding time series into images 通过将时间序列编码成图像来检测传感器数据中的异常情况

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-09 DOI: 10.1016/j.jksuci.2024.102232

Jidong Ma (继东) , Hairu Wang (王海茹)

Detecting anomalies in multivariate time series data is crucial for maintaining the optimal functionality of control system equipment. While existing research has made significant strides in this area, the increasing complexity of industrial environments poses challenges in accurately capturing the interactions between variables. Therefore, this paper introduces an innovative anomaly detection approach that extends one-dimensional time series into two-dimensions to capture the spatial correlations within the data. Unlike traditional approaches, we utilize the Gramian Angular Field to encode the correlations between different sensors at specific time points into images, enabling precise learning of spatial information across multiple variables. Subsequently, we construct an adversarial generative model to accurately identify anomalies at the pixel level, facilitating precise localization of abnormal points. We evaluate our method using five open-source datasets from various fields. Our method outperforms state-of-the-art anomaly detection techniques across all datasets, showcasing its superior performance. Particularly, our method achieves a 11.5% increase in F1 score on the high-dimensional WADI dataset compared to the baseline method. Additionally, we conduct thorough effectiveness analysis, parameter impact experiments, significant statistical analysis, and burden analysis, confirming the efficacy of our approach in capturing both the temporal dynamics and spatial relationships inherent in multivariate time series data.

检测多变量时间序列数据中的异常情况对于保持控制系统设备的最佳功能至关重要。虽然现有研究在这一领域取得了长足进步，但工业环境的日益复杂性给准确捕捉变量之间的相互作用带来了挑战。因此，本文引入了一种创新的异常检测方法，将一维时间序列扩展到二维，以捕捉数据中的空间相关性。与传统方法不同，我们利用格拉米安角场（Gramian Angular Field）将特定时间点上不同传感器之间的相关性编码成图像，从而实现跨多个变量的空间信息的精确学习。随后，我们构建了一个对抗生成模型，以准确识别像素级别的异常，从而促进异常点的精确定位。我们使用来自不同领域的五个开源数据集对我们的方法进行了评估。在所有数据集上，我们的方法都优于最先进的异常检测技术，展示了其卓越的性能。特别是，与基线方法相比，我们的方法在高维 WADI 数据集上的 F1 分数提高了 11.5%。此外，我们还进行了全面的有效性分析、参数影响实验、重要统计分析和负担分析，证实了我们的方法在捕捉多元时间序列数据中固有的时间动态和空间关系方面的功效。

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引用次数: 0

Semantic similarity on multimodal data: A comprehensive survey with applications 多模态数据的语义相似度：应用综述

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-12-09 DOI: 10.1016/j.jksuci.2024.102263

Baha Ihnaini , Belal Abuhaija , Ebenezer Atta Mills , Massudi Mahmuddin

Recently, the revival of the semantic similarity concept has been featured by the rapidly growing artificial intelligence research fueled by advanced deep learning architectures enabling machine intelligence using multimodal data. Thus, semantic similarity in multimodal data has gained substantial attention among researchers. However, the existing surveys on semantic similarity measures are restricted to a single modality, mainly text, which significantly limits the capability to understand the intelligence of real-world application scenarios. This study critically reviews semantic similarity approaches by shortlisting 223 vital articles from the leading databases and digital libraries to offer a comprehensive and systematic literature survey. The notable contribution is to illuminate the evolving landscape of semantic similarity and its crucial role in understanding, interpreting, and extracting meaningful information from multimodal data. Primarily, it highlights the challenges and opportunities inherent in different modalities, emphasizing the significance of advancements in cross-modal and multimodal semantic similarity approaches with potential application scenarios. Finally, the survey concludes by summarizing valuable future research directions. The insights provided in this survey improve the understanding and pave the way for further innovation by guiding researchers in leveraging the strength of semantic similarity for an extensive range of real-world applications.

最近，在先进的深度学习架构推动下，利用多模态数据实现机器智能的人工智能研究迅速发展，语义相似性概念也随之复兴。因此，多模态数据中的语义相似性受到了研究人员的极大关注。然而，现有的语义相似性测量研究仅限于单一模态，主要是文本，这极大地限制了理解真实世界应用场景智能的能力。本研究通过从主要数据库和数字图书馆中筛选出 223 篇重要文章，对语义相似性方法进行了批判性评述，从而提供了全面系统的文献调查。本研究的显著贡献在于阐明了语义相似性不断发展的现状及其在理解、解释和从多模态数据中提取有意义信息方面的关键作用。首先，它强调了不同模态固有的挑战和机遇，强调了跨模态和多模态语义相似性方法的进步与潜在应用场景的重要性。最后，调查报告总结了有价值的未来研究方向。本调查报告提供的真知灼见将指导研究人员利用语义相似性的优势为广泛的现实世界应用提供帮助，从而加深理解并为进一步创新铺平道路。

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引用次数: 0

An oversampling FCM-KSMOTE algorithm for imbalanced data classification 一种非平衡数据分类的过采样FCM-KSMOTE算法

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-28 DOI: 10.1016/j.jksuci.2024.102248

Hongfang Zhou , Jiahao Tong , Yuhan Liu , Kangyun Zheng , Chenhui Cao

In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose a novel oversampling method named FCM-KSMOTE. The algorithm initially performs a density-based fuzzy clustering on the data, then iterates to partition regions and perform oversampling inside each cluster. Secondly, it merges the clusters and conducts noise detection to obtain a balanced dataset. Finally, we conducted the experiments on 19 public datasets and 3 synthetic datasets. Six evaluation metrics of Recall, Accuracy, G-mean, Specificity, AUC and F1-Score were used in the experiments. The experimental results demonstrate that our method can significantly improve the recognition rate of the minority class while maintaining high accuracy for the majority class. Particularly with the RF classifier, our method ranks first in all evaluation metrics, with a Recall difference of up to 0.2 compared to the least performing method, demonstrating its substantial performance advantage.

近年来，不平衡数据分类已成为一项具有挑战性的任务。针对这一问题，我们提出了一种名为 FCM-KSMOTE 的新型超采样方法。该算法首先对数据进行基于密度的模糊聚类，然后迭代划分区域，并在每个聚类内部进行超采样。其次，该算法合并聚类并进行噪声检测，以获得平衡的数据集。最后，我们在 19 个公共数据集和 3 个合成数据集上进行了实验。实验中使用了 Recall、Accuracy、G-mean、Specificity、AUC 和 F1-Score 六个评价指标。实验结果表明，我们的方法可以显著提高少数人类别的识别率，同时保持多数人类别的高准确率。特别是在 RF 分类器方面，我们的方法在所有评价指标中都名列第一，与表现最差的方法相比，我们的方法的 Recall 差值高达 0.2，显示了其巨大的性能优势。

引用次数: 0

Deep reinforcement learning-based local path planning in dynamic environments for mobile robot 动态环境下基于深度强化学习的移动机器人局部路径规划

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-22 DOI: 10.1016/j.jksuci.2024.102254

Bodong Tao, Jae-Hoon Kim

Path planning for robots in dynamic environments is a challenging task, as it requires balancing obstacle avoidance, trajectory smoothness, and path length during real-time planning.This paper proposes an algorithm called Adaptive Soft Actor–Critic (ASAC), which combines the Soft Actor–Critic (SAC) algorithm, tile coding, and the Dynamic Window Approach (DWA) to enhance path planning capabilities. ASAC leverages SAC with an automatic entropy adjustment mechanism to balance exploration and exploitation, integrates tile coding for improved feature representation, and utilizes DWA to define the action space through parameters such as target heading, obstacle distance, and velocity In this framework, the action space is defined by DWA’s three weighting parameters: target heading deviation, distance to the nearest obstacle, and velocity. To facilitate the learning process, a non-sparse reward function is designed, incorporating factors such as Time-to-Collision (TTC), heading, and velocity. To validate the effectiveness of the algorithm, experiments were conducted in four different environments, and the algorithm was evaluated based on metrics such as trajectory deviation, smoothness, and time to reach the end point. The results demonstrate that ASAC outperforms existing algorithms in terms of trajectory smoothness, arrival time, and overall adaptability across various scenarios, effectively enabling path planning in dynamic environments.

动态环境下的机器人路径规划是一项具有挑战性的任务，因为它需要在实时规划过程中平衡避障、轨迹平滑和路径长度。本文提出了一种自适应软行为者批评家（ASAC）算法，该算法结合了软行为者批评家（SAC）算法、贴图编码和动态窗口方法（DWA）来增强路径规划能力。ASAC利用带有自动熵调整机制的SAC来平衡探索和利用，集成瓦片编码来改进特征表示，并利用DWA通过目标航向、障碍物距离和速度等参数来定义动作空间。在该框架中，动作空间由DWA的三个加权参数：目标航向偏差、到最近障碍物的距离和速度来定义。为了方便学习过程，设计了一个非稀疏奖励函数，结合了碰撞时间（TTC）、航向和速度等因素。为了验证算法的有效性，在四种不同的环境下进行了实验，并根据轨迹偏差、平滑度和到达终点时间等指标对算法进行了评估。结果表明，ASAC在轨迹平滑度、到达时间和各种场景的整体适应性方面优于现有算法，有效地实现了动态环境下的路径规划。

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引用次数: 0

Real-time semantic segmentation for autonomous driving: A review of CNNs, Transformers, and Beyond 用于自动驾驶的实时语义分割：CNN、变形器及其他技术综述

IF 5.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of King Saud University-Computer and Information Sciences

Pub Date : 2024-12-01 Epub Date: 2024-11-04 DOI: 10.1016/j.jksuci.2024.102226

Mohammed A.M. Elhassan , Changjun Zhou , Ali Khan , Amina Benabid , Abuzar B.M. Adam , Atif Mehmood , Naftaly Wambugu

Real-time semantic segmentation is a crucial component of autonomous driving systems, where accurate and efficient scene interpretation is essential to ensure both safety and operational reliability. This review provides an in-depth analysis of state-of-the-art approaches in real-time semantic segmentation, with a particular focus on Convolutional Neural Networks (CNNs), Transformers, and hybrid models. We systematically evaluate these methods and benchmark their performance in terms of frames per second (FPS), memory consumption, and CPU runtime. Our analysis encompasses a wide range of architectures, highlighting their novel features and the inherent trade-offs between accuracy and computational efficiency. Additionally, we identify emerging trends, and propose future directions to advance the field. This work aims to serve as a valuable resource for both researchers and practitioners in autonomous driving, providing a clear roadmap for future developments in real-time semantic segmentation. More resources and updates can be found at our GitHub repository: https://github.com/mohamedac29/Real-time-Semantic-Segmentation-Survey

实时语义分割是自动驾驶系统的重要组成部分，准确高效的场景解读对确保安全和运行可靠性至关重要。本综述深入分析了最先进的实时语义分割方法，尤其关注卷积神经网络（CNN）、变形器和混合模型。我们系统地评估了这些方法，并根据每秒帧数（FPS）、内存消耗和 CPU 运行时间对其性能进行了基准测试。我们的分析涵盖了各种架构，突出了它们的新特点以及准确性和计算效率之间的内在权衡。此外，我们还确定了新兴趋势，并提出了推动该领域发展的未来方向。这项工作旨在为自动驾驶领域的研究人员和从业人员提供宝贵的资源，为实时语义分割的未来发展提供清晰的路线图。更多资源和更新请访问我们的 GitHub 存储库：https://github.com/mohamedac29/Real-time-Semantic-Segmentation-Survey

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引用次数: 0