Journal of King Saud University-Computer and Information Sciences最新文献_第4页

Low-light image enhancement: A comprehensive review on methods, datasets and evaluation metrics 弱光图像增强：方法、数据集和评估指标综合评述

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 : 2024-11-05 DOI: 10.1016/j.jksuci.2024.102234

Zhan Jingchun , Goh Eg Su , Mohd Shahrizal Sunar

Enhancing low-light images in computer vision is a significant challenge that requires innovative methods to improve its robustness. Low-light image enhancement (LLIE) enhances the quality of images affected by poor lighting conditions by implementing various loss functions such as reconstruction, perceptual, smoothness, adversarial, and exposure. This review analyses and compares different methods, ranging from traditional to cutting-edge deep learning methods, showcasing the significant advancements in the field. Although similar reviews have been studied on LLIE, this paper not only updates the knowledge but also focuses on recent deep learning methods from various perspectives or interpretations. The methodology used in this paper compares different methods from the literature and identifies the potential research gaps. This paper highlights the recent advancements in the field by classifying them into three classes, demonstrated by the continuous enhancements in LLIE methods. These improved methods use different loss functions showing higher efficacy through metrics such as Peak Signal-to-Noise Ratio, Structural Similarity Index Measure, and Naturalness Image Quality Evaluator. The research emphasizes the significance of advanced deep learning techniques and comprehensively compares different LLIE methods on various benchmark image datasets. This research is a foundation for scientists to illustrate potential future research directions.

在计算机视觉中增强低照度图像是一项重大挑战，需要创新方法来提高其鲁棒性。低照度图像增强（LLIE）通过实施各种损失函数（如重建、感知、平滑度、对抗和曝光）来提高受低照度条件影响的图像质量。本综述分析并比较了从传统方法到前沿深度学习方法等不同方法，展示了该领域的重大进展。虽然类似的综述已对 LLIE 进行了研究，但本文不仅更新了相关知识，还从不同的角度或解释关注了最新的深度学习方法。本文采用的方法比较了文献中的不同方法，并找出了潜在的研究空白。本文重点介绍了该领域的最新进展，将其分为三类，并通过 LLIE 方法的不断改进加以展示。这些改进方法使用不同的损失函数，通过峰值信噪比、结构相似性指数测量和自然度图像质量评估器等指标显示出更高的功效。研究强调了先进深度学习技术的重要性，并在各种基准图像数据集上全面比较了不同的 LLIE 方法。这项研究为科学家说明未来潜在的研究方向奠定了基础。

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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 : 2024-11-05 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

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 : 2024-11-04 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

TFDNet: A triple focus diffusion network for object detection in urban congestion with accurate multi-scale feature fusion and real-time capability TFDNet：用于城市拥堵路段物体检测的三重聚焦扩散网络，具有精确的多尺度特征融合和实时能力

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 : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102223

Caoyu Gu , Xiaodong Miao , Chaojie Zuo

Vehicle detection in congested urban scenes is essential for traffic control and safety management. However, the dense arrangement and occlusion of multi-scale vehicles in such environments present considerable challenges for detection systems. To tackle these challenges, this paper introduces a novel object detection method, dubbed the triple focus diffusion network (TFDNet). Firstly, the gradient convolution is introduced to construct the C2f-EIRM module, replacing the original C2f module, thereby enhancing the network’s capacity to extract edge information. Secondly, by leveraging the concept of the Asymptotic Feature Pyramid Network on the foundation of the Path Aggregation Network, the triple focus diffusion module structure is proposed to improve the network’s ability to fuse multi-scale features. Finally, the SPPF-ELA module employs an Efficient Local Attention mechanism to integrate multi-scale information, thereby significantly reducing the impact of background noise on detection accuracy. Experiments on the VisDrone 2021 dataset reveal that the average detection accuracy of the TFDNet algorithm reached 38.4%, which represents a 6.5% improvement over the original algorithm; similarly, its mAP50:90 performance has increased by 3.7%. Furthermore, on the UAVDT dataset, the TFDNet achieved a 3.3% enhancement in performance compared to the original algorithm. TFDNet, with a processing speed of 55.4 FPS, satisfies the real-time requirements for vehicle detection.

在拥堵的城市场景中进行车辆检测对于交通管制和安全管理至关重要。然而，在这种环境中，多尺度车辆的密集排列和遮挡给检测系统带来了相当大的挑战。为了应对这些挑战，本文介绍了一种新颖的物体检测方法，即三重聚焦扩散网络（TFDNet）。首先，引入梯度卷积来构建 C2f-EIRM 模块，取代原有的 C2f 模块，从而增强网络提取边缘信息的能力。其次，在路径聚合网络的基础上，利用渐近特征金字塔网络的概念，提出了三重焦点扩散模块结构，提高了网络融合多尺度特征的能力。最后，SPPF-ELA 模块采用高效局部关注机制来整合多尺度信息，从而显著降低背景噪声对检测精度的影响。在 VisDrone 2021 数据集上的实验表明，TFDNet 算法的平均检测准确率达到了 38.4%，比原始算法提高了 6.5%；同样，其 mAP50:90 性能也提高了 3.7%。此外，在 UAVDT 数据集上，TFDNet 的性能比原始算法提高了 3.3%。TFDNet 的处理速度为 55.4 FPS，满足了车辆检测的实时要求。

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引用次数: 0

Corrigendum to “Effective and scalable black-box fuzzing approach for modern web applications” [J. King Saud Univ. Comp. Info. Sci. 34(10) (2022) 10068–10078] 现代网络应用的有效和可扩展黑盒模糊方法"[J. King Saud Univ. Comp. Info. Sci. 34(10) (2022) 10068-10078] 更正

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 : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102216

Aseel Alsaedi, Abeer Alhuzali, Omaimah Bamasag

引用次数: 0

DNE-YOLO: A method for apple fruit detection in Diverse Natural Environments DNE-YOLO：在多样化自然环境中检测苹果果实的方法

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 : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102220

Haitao Wu , Xiaotian Mo , Sijian Wen , Kanglei Wu , Yu Ye , Yongmei Wang , Youhua Zhang

The apple industry, recognized as a pivotal sector in agriculture, increasingly emphasizes the mechanization and intelligent advancement of picking technology. This study innovatively applies a mist simulation algorithm to apple image generation, constructing a dataset of apple images under mixed sunny, cloudy, drizzling and foggy weather conditions called DNE-APPLE. It introduces a lightweight and efficient target detection network called DNE-YOLO. Building upon the YOLOv8 base model, DNE-YOLO incorporates the CBAM attention mechanism and CARAFE up-sampling operator to enhance the focus on apples. Additionally, it utilizes GSConv and the dynamic non-monotonic focusing mechanism loss function WIOU to reduce model parameters and decrease reliance on dataset quality. Extensive experimental results underscore the efficacy of the DNE-YOLO model, which achieves a detection accuracy (precision) of 90.7%, a recall of 88.9%, a mean accuracy (mAP50) of 94.3%, a computational complexity (GFLOPs) of 25.4G, and a parameter count of 10.46M across various environmentally diverse datasets. Compared to YOLOv8, it exhibits superior detection accuracy and robustness in sunny, drizzly, cloudy, and misty environments, making it especially suitable for practical applications such as apple picking for agricultural robots. The code for this model is open source at https://github.com/wuhaitao2178827/DNE-YOLO.

苹果产业作为农业中举足轻重的行业，越来越重视采摘技术的机械化和智能化。本研究创新性地将雾气模拟算法应用于苹果图像生成，构建了一个名为 DNE-APPLE 的晴天、多云、小雨和大雾混合天气条件下的苹果图像数据集。它引入了一种名为 DNE-YOLO 的轻量级高效目标检测网络。在 YOLOv8 基本模型的基础上，DNE-YOLO 加入了 CBAM 注意机制和 CARAFE 上采样算子，以加强对苹果的关注。此外，它还利用 GSConv 和动态非单调聚焦机制损失函数 WIOU 来减少模型参数，降低对数据集质量的依赖。广泛的实验结果证明了 DNE-YOLO 模型的有效性，它在各种不同环境的数据集上实现了 90.7% 的检测准确率（精确度）、88.9% 的召回率、94.3% 的平均准确率（mAP50）、25.4G 的计算复杂度（GFLOPs）和 10.46M 的参数数。与 YOLOv8 相比，它在晴天、小雨、多云和雾霾环境中都表现出了更高的检测精度和鲁棒性，因此特别适合农业机器人采摘苹果等实际应用。该模型的代码开源于 https://github.com/wuhaitao2178827/DNE-YOLO。

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引用次数: 0

Energy-efficient resource allocation for UAV-aided full-duplex OFDMA wireless powered IoT communication networks 无人机辅助全双工 OFDMA 无线供电物联网通信网络的高能效资源分配

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 : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102225

Tong Wang

The rapid development of wireless-powered Internet of Things (IoT) networks, supported by multiple unmanned aerial vehicles (UAVs) and full-duplex technologies, has opened new avenues for simultaneous data transmission and energy harvesting. In this context, optimizing energy efficiency (EE) is crucial for ensuring sustainable and efficient network operation. This paper proposes a novel approach to EE optimization in multi-UAV-aided wireless-powered IoT networks, focusing on balancing the uplink data transmission rates and total system energy consumption within an orthogonal frequency-division multiple access (OFDMA) framework. This involves formulating the EE optimization problem as a Multi-Objective Optimization Problem (MOOP), consisting of the maximization of the uplink total rate and the minimization of the total system energy consumption, which is then transformed into a Single-Objective Optimization Problem (SOOP) using the Tchebycheff method. To address the non-convex nature of the resulting SOOP, characterized by combinatorial variables and coupled constraints, we developed an iterative algorithm that combines Block Coordinate Descent (BCD) with Successive Convex Approximation (SCA). This algorithm decouples the subcarrier assignment and power control subproblems, incorporates a penalty term to relax integer constraints, and alternates between solving each subproblem until convergence is reached. Simulation results demonstrate that our proposed method outperforms baseline approaches in key performance metrics, highlighting the practical applicability and robustness of our framework for enhancing the efficiency and sustainability of real-world UAV-assisted wireless networks. Our findings provide insights for future research on extending the proposed framework to scenarios involving dynamic UAV mobility, multi-hop communication, and enhanced energy management, thereby supporting the development of next-generation sustainable communication systems.

在多种无人飞行器（UAV）和全双工技术的支持下，无线供电的物联网（IoT）网络发展迅速，为同时进行数据传输和能量采集开辟了新的途径。在这种情况下，优化能源效率（EE）对于确保网络的可持续高效运行至关重要。本文提出了一种在多无人机辅助的无线供电物联网网络中优化能效的新方法，重点是在正交频分多址（OFDMA）框架内平衡上行数据传输速率和系统总能耗。这涉及将 EE 优化问题表述为多目标优化问题（MOOP），包括上行链路总速率最大化和系统总能耗最小化，然后使用 Tchebycheff 方法将其转化为单目标优化问题（SOOP）。为了解决以组合变量和耦合约束为特征的 SOOP 的非凸性质，我们开发了一种结合了块坐标下降 (BCD) 和连续凸逼近 (SCA) 的迭代算法。该算法将子载波分配和功率控制子问题分离开来，加入惩罚项以放松整数约束，并交替解决每个子问题，直至达到收敛。仿真结果表明，我们提出的方法在关键性能指标上优于基准方法，突出了我们的框架在提高现实世界无人机辅助无线网络的效率和可持续性方面的实际适用性和稳健性。我们的研究结果为未来研究提供了启示，有助于将所提出的框架扩展到涉及无人机动态移动性、多跳通信和增强能源管理的场景，从而支持下一代可持续通信系统的开发。

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引用次数: 0

General secure encryption algorithm for separable reversible data hiding in encrypted domain 加密域中可分离可逆数据隐藏的通用安全加密算法

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 : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102217

Hongli Wan, Minqing Zhang, Yan Ke, Zongbao Jiang, Fuqiang Di

The separable reversible data hiding in encrypted domain (RDH-ED) algorithm leaves out the embedding space for the information before or after encryption and makes the operation of extracting the information and restoring the image not interfere with each other. The encryption method employed not only affects the embedding space of the information and separability, but is more crucial for ensuring security. However, the commonly used XOR, scram-bling or combination methods fall short in security, especially against known plaintext attack (KPA). Therefore, in order to improve the security of RDH-ED and be widely applicable, this paper proposes a high-security RDH-ED encryption algorithm that can be used to reserve space before encryption (RSBE) and free space after encryption (FSAE). During encryption, the image undergoes block XOR, global intra-block bit-plane scrambling (GIBS) and inter-block scrambling sequentially. The GIBS key is created through chaotic mapping transformation. Subsequently, two RDH-ED algorithms based on this encryption are proposed. Experimental results indicate that the algorithm outlined in this paper maintains consistent key communication traffic post key conversion. Additionally, its computational complexity remains at a constant level, satisfying separability criteria, and is suitable for both RSBE and FSAE methods. Simultaneously, while satisfying the security of a single encryption technique, we have expanded the key space to 2

^{8 N p} \times N p! \times 8!^{N p}

, enabling resilience against various existing attack methods. Notably, particularly in KPA testing scenarios, the average decryption success rate is a mere 0.0067% and 0.0045%, highlighting its exceptional security. Overall, this virtually unbreakable system significantly enhances image security while preserving an appropriate embedding capacity.

加密域中的可分离可逆数据隐藏（RDH-ED）算法在加密前后都留出了信息的嵌入空间，使提取信息和还原图像的操作互不干扰。所采用的加密方法不仅会影响信息的嵌入空间和可分离性，而且对确保安全性更为关键。然而，常用的 XOR、加扰或组合方法在安全性方面存在不足，尤其是在应对已知明文攻击（KPA）时。因此，为了提高 RDH-ED 的安全性和广泛适用性，本文提出了一种可用于加密前预留空间（RSBE）和加密后释放空间（FSAE）的高安全性 RDH-ED 加密算法。在加密过程中，图像依次经过块 XOR、全局块内位平面加扰（GIBS）和块间加扰。GIBS 密钥通过混沌映射变换创建。随后，提出了两种基于这种加密的 RDH-ED 算法。实验结果表明，本文概述的算法能在密钥转换后保持一致的密钥通信流量。此外，该算法的计算复杂度保持在恒定水平，满足可分性标准，同时适用于 RSBE 和 FSAE 方法。同时，在满足单一加密技术安全性的同时，我们还将密钥空间扩展到了 28Np×Np！×8！Np，从而能够抵御现有的各种攻击方法。值得注意的是，特别是在 KPA 测试场景中，平均解密成功率仅为 0.0067% 和 0.0045%，彰显了其卓越的安全性。总之，这个几乎牢不可破的系统在保持适当嵌入容量的同时，大大增强了图像的安全性。

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引用次数: 0

Quantum computing enhanced knowledge tracing: Personalized KT research for mitigating data sparsity 量子计算增强知识追踪：缓解数据稀疏性的个性化 KT 研究

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 : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102224

Chengke Bao , Qianxi Wu , Weidong Ji , Min Wang , Haoyu Wang

With the development of artificial intelligence in education, knowledge tracing (KT) has become a current research hotspot and is the key to the success of personalized instruction. However, data sparsity remains a significant challenge in the KT domain. To address this challenge, this paper applies quantum computing (QC) technology to KT for the first time. It proposes two personalized KT models incorporating quantum mechanics (QM): quantum convolutional enhanced knowledge tracing (QCE-KT) and quantum variational enhanced knowledge tracing (QVE-KT). Through quantum superposition and entanglement properties, QCE-KT and QVE-KT effectively alleviate the data sparsity problem in the KT domain through quantum convolutional layers and variational quantum circuits, respectively, and significantly improve the quality of the representation and prediction accuracy of students’ knowledge states. Experiments on three datasets show that our models outperform ten benchmark models. On the most sparse dataset, QCE-KT and QVE-KT improve their performance by 16.44% and 14.78%, respectively, compared to DKT. Although QC is still in the developmental stage, this study reveals the great potential of QM in personalized KT, which provides new perspectives for solving personalized instruction problems and opens up new directions for applying QC in education.

随着人工智能在教育领域的发展，知识追踪（KT）已成为当前的研究热点，也是个性化教学成功的关键。然而，数据稀疏性仍然是知识追踪领域的一个重大挑战。为应对这一挑战，本文首次将量子计算（QC）技术应用于 KT。它提出了两种结合量子力学（QM）的个性化知识追踪模型：量子卷积增强知识追踪（QCE-KT）和量子变分增强知识追踪（QVE-KT）。通过量子叠加和纠缠特性，QCE-KT 和 QVE-KT 分别通过量子卷积层和量子变分电路有效缓解了知识追踪领域的数据稀疏性问题，显著提高了学生知识状态的表征质量和预测精度。三个数据集的实验表明，我们的模型优于十个基准模型。在最稀疏的数据集上，QCE-KT 和 QVE-KT 的性能比 DKT 分别提高了 16.44% 和 14.78%。虽然 QC 仍处于发展阶段，但本研究揭示了 QM 在个性化 KT 中的巨大潜力，为解决个性化教学问题提供了新的视角，也为 QC 在教育领域的应用开辟了新的方向。

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引用次数: 0

DA-Net: A classification-guided network for dental anomaly detection from dental and maxillofacial images DA-Net：从牙科和颌面部图像中检测牙科异常的分类指导网络

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 : 2024-11-01 DOI: 10.1016/j.jksuci.2024.102229

Jiaxing Li

Dental abnormalities (DA) are frequent signs of disorders of the mouth that cause discomfort, infection, and loss of teeth. Early and reasonably priced treatment may be possible if defective teeth in the oral cavity are automatically detected. Several research works have endeavored to create a potent deep learning model capable of identifying DA from pictures. However, because of the following problems, aberrant teeth from the oral cavity are difficult to detect: 1) Normal teeth and crowded dentition frequently overlap; 2) The lesion area on the tooth surface is tiny. This paper proposes a professional dental anomaly detection network (DA-Net) to address such issues. First, a multi-scale dense connection module (MSDC) is designed to distinguish crowded teeth from normal teeth by learning multi-scale spatial information of dentition. Then, a pixel differential convolution (PDC) module is designed to perform pathological tooth recognition by extracting small lesion features. Finally, a multi-stage convolutional attention module (MSCA) is developed to integrate spatial information and channel information to obtain abnormal teeth in small areas. Experiments on benchmarks show that DA-Net performs well in dental anomaly detection and can further assist doctors in making treatment plans. Specifically, the DA-Net method performs best on multiple detection evaluation metrics: IoU, PRE, REC, and mAP. In terms of REC and mAP indicators, the proposed DA-Net method is 1.1% and 1.3% higher than the second-ranked YOLOv7 method.

牙齿异常（DA）是口腔疾病的常见征兆，会引起不适、感染和牙齿脱落。如果能自动检测出口腔中存在缺陷的牙齿，就可以及早进行价格合理的治疗。一些研究工作致力于创建一个强大的深度学习模型，能够从图片中识别牙齿缺损。然而，由于以下问题，口腔畸形牙难以检测：1）正常牙齿和拥挤牙经常重叠；2）牙齿表面的病变面积很小。针对这些问题，本文提出了一种专业的牙齿异常检测网络（DA-Net）。首先，设计了一个多尺度密集连接模块（MSDC），通过学习牙列的多尺度空间信息来区分拥挤牙和正常牙。然后，设计了一个像素差分卷积（PDC）模块，通过提取小病变特征来进行病牙识别。最后，开发了多级卷积注意力模块（MSCA），以整合空间信息和通道信息，从而获得小区域的异常牙齿。基准实验表明，DA-Net 在牙齿异常检测方面表现出色，可以进一步帮助医生制定治疗方案。具体来说，DA-Net 方法在多个检测评估指标上表现最佳：IoU、PRE、REC 和 mAP。在 REC 和 mAP 指标上，DA-Net 方法比排名第二的 YOLOv7 方法分别高出 1.1% 和 1.3%。

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引用次数: 0