Engineering Applications of Artificial Intelligence最新文献

Constrained multi-objective optimization assisted by convergence and diversity auxiliary tasks 收敛和多样性辅助任务辅助的受限多目标优化

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-06 DOI: 10.1016/j.engappai.2024.109546

In the field of constrained multi-objective optimization, constructing auxiliary tasks can guide the algorithm to achieve efficient search. Different forms of auxiliary tasks have their own advantages, and a reasonable combination can effectively improve the performance of the algorithm. Inspired by this, a Constrained Multi-objective Optimization Evolutionary Algorithm based on Convergence and Diversity auxiliary Tasks (CMOEA-CDT) is proposed. This algorithm achieves efficient search through simultaneous optimization and knowledge transfer of the main task, convergence auxiliary task, and diversity auxiliary task. Specifically, the main task is to find feasible Pareto front, which improves the global exploration and local exploitation of the algorithm through knowledge transfer from the convergence and diversity auxiliary tasks. In addition, the convergence auxiliary task helps the main task population traverse infeasible obstacles by ignoring constraints to achieve global search. The diversity auxiliary task aims to provide local diversity to the regions around the main task population to exploit promising search regions. The convergence and diversity of the algorithm are significantly improved by knowledge transfer between the convergence auxiliary task, diversity auxiliary task, and main task. CMOEA-CDT is compared with five state-of-the-art constrained multi-objective evolutionary optimization algorithms on 37 benchmark problems and a disc brake engineering design problem. The experimental results indicate that the proposed CMOEA-CDT respectively obtains 19 and 20 best results on the two indicators, and achieves the best performance on disc brake engineering design problem.

在约束多目标优化领域，构建辅助任务可以引导算法实现高效搜索。不同形式的辅助任务各有优势，合理组合能有效提高算法性能。受此启发，本文提出了一种基于收敛和多样性辅助任务的约束多目标优化进化算法（CMOEA-CDT）。该算法通过主任务、收敛辅助任务和多样性辅助任务的同步优化和知识转移实现高效搜索。具体来说，主任务是寻找可行的帕累托前沿，通过收敛辅助任务和多样性辅助任务的知识转移，提高算法的全局探索和局部开发能力。此外，收敛辅助任务通过忽略约束条件来帮助主任务群体穿越不可行障碍，从而实现全局搜索。多样性辅助任务旨在为主要任务群周围的区域提供局部多样性，以利用有希望的搜索区域。通过收敛性辅助任务、多样性辅助任务和主任务之间的知识转移，算法的收敛性和多样性得到了显著提高。在 37 个基准问题和一个盘式制动器工程设计问题上，CMOEA-CDT 与五种最先进的约束多目标进化优化算法进行了比较。实验结果表明，所提出的 CMOEA-CDT 在两个指标上分别取得了 19 分和 20 分的最佳结果，并在盘式制动器工程设计问题上取得了最佳性能。

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引用次数: 0

A Chinese named entity recognition method for landslide geological disasters based on deep learning 基于深度学习的滑坡地质灾害中文命名实体识别方法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-05 DOI: 10.1016/j.engappai.2024.109537

Landslide Named Entity Recognition (LNER) involves extracting specific entities from Chinese unstructured landslide disaster texts, which is crucial for constructing a knowledge graph and supporting landslide prevention efforts. This study proposes a deep learning-based LNER model that utilizes Bidirectional Encoder Representations from Transformer (BERT) for word embeddings and integrates the Conditional Random Fields (CRF) algorithm and projected gradient descent (PGD) adversarial neural networks to enhance sequence labeling accuracy. The practical implications of this research lie in improving the efficiency and precision of disaster information extraction, aiding in real-time decision-making and risk mitigation strategies. Experiments on the constructed dataset show that the model effectively identifies eight types of landslide entities, achieving a highest F1 score of 89.7%.

滑坡命名实体识别（LNER）涉及从中文非结构化滑坡灾害文本中提取特定实体，这对于构建知识图谱和支持滑坡预防工作至关重要。本研究提出了一种基于深度学习的 LNER 模型，该模型利用变换器的双向编码器表示（BERT）进行词嵌入，并集成了条件随机场（CRF）算法和投射梯度下降（PGD）对抗神经网络，以提高序列标注的准确性。这项研究的实际意义在于提高灾害信息提取的效率和精确度，帮助制定实时决策和风险缓解策略。在构建的数据集上进行的实验表明，该模型能有效识别八种类型的滑坡实体，最高 F1 得分为 89.7%。

引用次数: 0

A deep sequence-to-sequence model for power swing blocking of distance protection in power transmission lines 输电线路距离保护功率摆动闭锁的深度序列到序列模型

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-05 DOI: 10.1016/j.engappai.2024.109538

As the primary protection method for transmission lines, distance relays are prone to malfunction during power swings. In fact, the inability of distance relays to differentiate between power swings and short-circuit faults imposes a significant risk to power system stability that can result in blackouts. In recent years, there has been increasing interest in leveraging machine learning techniques to identify various types of faults and power swings in electrical systems. However, previous works mainly focus on fault classification, which is mostly done after a long period from the moment of fault initiation. This is the reason for requiring extensive post-fault data for diagnosis. To address this challenge, this study proposes a predictive protection strategy utilizing deep learning methodologies, specifically a sequence-to-sequence model, to monitor electrical power systems continuously. The objective is to effectively detect power swings from short-circuit faults with minimal reliance on post-fault data and accurately identify short-circuit faults during power swings. In the proposed approach, features are extracted from grid current signals using the Hilbert transform and empirical mode decomposition algorithms. These features are then fed into the sequence-to-sequence model, which issues block/unblock commands upon confirming the presence of a power swing or fault during the power swing. Results from various simulations conducted on an IEEE 39-bus grid in DIgSILENT and MATLAB environments demonstrate that the proposed scheme outperforms baseline methods in the detection of short-circuit faults, power swings, and short-circuit faults occurring during the power swings. The timely and correct operation of the proposed protection scheme contributes to the stability of transmission lines and power systems.

作为输电线路的主要保护方式，距离继电器很容易在功率波动时发生故障。事实上，距离继电器无法区分功率波动和短路故障，这给电力系统的稳定性带来了巨大风险，可能导致停电。近年来，人们越来越关注利用机器学习技术来识别电力系统中的各类故障和功率波动。然而，以往的工作主要集中在故障分类上，而故障分类大多是在故障发生后的很长一段时间内完成的。这就是诊断需要大量故障后数据的原因。为了应对这一挑战，本研究提出了一种预测性保护策略，利用深度学习方法，特别是序列到序列模型，对电力系统进行持续监控。目标是在尽量不依赖故障后数据的情况下，有效检测短路故障引起的功率波动，并在功率波动期间准确识别短路故障。在所提出的方法中，使用希尔伯特变换和经验模式分解算法从电网电流信号中提取特征。然后将这些特征输入序列到序列模型，该模型在确认存在功率摆动或功率摆动期间出现故障时发出闭锁/解闭锁指令。在 DIgSILENT 和 MATLAB 环境中对 IEEE 39 总线电网进行的各种仿真结果表明，在检测短路故障、功率摆动和功率摆动期间发生的短路故障方面，所提出的方案优于基准方法。拟议保护方案的及时正确运行有助于提高输电线路和电力系统的稳定性。

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引用次数: 0

Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection 利用遗传算法引导的关键像素选择实现图像存储记忆效率的紧凑表示法

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109540

In the past few years, we have observed rapid growth in digital content. Even in the biological domain, the arrival of microscopic and nanoscopic images and videos captured for biological investigations increases the need for space to store them. Hence, storing these data in a storage-efficient manner is a pressing need. In this work, we have introduced a compact image representation technique with an eye on preserving the shape that can shrink the memory requirement to store. The compact image representation is different from image compression since it does not include any encoding mechanism. Rather, the idea is that this mechanism stores the positions of key pixels, and when required, the original image can be regenerated. The genetic algorithm is used to select key pixels, while the Gaussian kernel performs the reconstruction task with the help of the positions of the selected key pixels. The model is tested on four different datasets. The proposed technique shrinks the memory requirement by 87% to 98% while evaluated using the bit reduction rate. However, the reconstructed images’ quality is a bit low when evaluated using metrics like structural similarity index (ranges between 0.81 to 0.94), or root means squared error (ranges between 0.06 to 0.08). To investigate the impact of quality reduction in reconstructed images in real-life applications, we performed image classification using reconstructed samples and found 0.13% to 2.30% classification accuracy reduction compared to when classification is done using original samples. The proposed model’s performance is comparable to state-of-the-art’s similar solutions.

在过去几年中，我们注意到数字内容的快速增长。即使在生物领域，用于生物研究的微观和纳米图像及视频的出现也增加了对存储空间的需求。因此，以存储效率高的方式存储这些数据已成为当务之急。在这项工作中，我们引入了一种紧凑型图像表示技术，该技术着眼于保留图像的形状，可以减少存储所需的内存。紧凑图像表示法不同于图像压缩，因为它不包括任何编码机制。相反，这种机制的理念是存储关键像素的位置，在需要时，可以重新生成原始图像。遗传算法用于选择关键像素，而高斯核则借助所选关键像素的位置执行重建任务。该模型在四个不同的数据集上进行了测试。在使用比特减少率进行评估时，建议的技术将内存需求减少了 87% 至 98%。然而，在使用结构相似性指数（范围在 0.81 到 0.94 之间）或均方根误差（范围在 0.06 到 0.08 之间）等指标进行评估时，重建图像的质量有点低。为了研究重构图像质量下降在实际应用中的影响，我们使用重构样本进行了图像分类，发现与使用原始样本进行分类相比，分类准确率下降了 0.13% 到 2.30%。建议模型的性能可与最先进的类似解决方案相媲美。

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引用次数: 0

Color-aware fusion of nighttime infrared and visible images 夜间红外图像与可见光图像的色彩感知融合

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109521

Pixel-level fusion of visible and infrared images has demonstrated promise in enhancing information representation. However, nighttime image fusion remains challenging due to low and uneven lighting. Existing fusion methods neglect the preservation of color-related information at night, resulting in unsatisfactory outcomes with insufficient brightness. This paper presents a novel color image fusion framework to prevent color distortion, thus generating results more aligned with human perception. Firstly, we design an image fusion network to retain color information from visible images under low-light conditions. Secondly, we incorporate mature low-light enhancement technology into the network as a flexible component to produce fusion results under normal illumination. The training process is carefully designed to address potential issues of overexposure or noise amplification. Finally, we utilize knowledge distillation to create a lightweight end-to-end network that directly generates fusion results under normal lighting conditions from pairs of low-light images. Experimental results demonstrate that our proposed framework outperforms existing methods in nighttime scenarios.

可见光和红外图像的像素级融合在增强信息表现力方面大有可为。然而，由于光照度低且不均匀，夜间图像融合仍具有挑战性。现有的融合方法忽视了夜间色彩相关信息的保存，导致亮度不足，效果不尽人意。本文提出了一种新颖的彩色图像融合框架，以防止色彩失真，从而产生更符合人类感知的结果。首先，我们设计了一个图像融合网络，以保留低照度条件下可见光图像中的色彩信息。其次，我们将成熟的弱光增强技术融入网络，作为一个灵活的组件，在正常光照条件下生成融合结果。训练过程经过精心设计，以解决潜在的曝光过度或噪声放大问题。最后，我们利用知识提炼技术创建了一个轻量级端到端网络，可在正常光照条件下直接生成低照度图像对的融合结果。实验结果表明，我们提出的框架在夜间场景中的表现优于现有方法。

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引用次数: 0

Combining model-based and learning-based anomaly detection schemes for increased performance and safety of aircraft braking controllers 结合基于模型和基于学习的异常检测方案，提高飞机制动控制器的性能和安全性

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109551

In aircraft, the braking system is a safety-critical and heavily used component of the landing gear, prone to significant wear. Anomalies arising in the wear dynamics can degrade the performance of the braking system and compromise the safety of ground handling maneuvers. In this work, we tackle the problem of detecting incipient anomalies in aircraft brakes in a tightly coupled implementation with the Brake Control Unit (BCU). Two complementary approaches are presented. The first one is an observer-based architecture designed on the longitudinal aircraft dynamics that returns physically interpretable outputs connected to the wear process and allows us to improve braking performance online. The second one is an end-to-end convolutional autoencoder-based architecture that returns an anomaly score computed on data collected by the BCU with inherent robustness to modeling uncertainty, which the model-based one does not. A combined architecture that allows one to exploit the features of both model-based and learning-based approaches is proposed, which shows its capability of optimally blending the two. The approaches are evaluated in a MATLAB/Simulink multibody simulation environment that is able to replicate the braking actuator wear dynamics, demonstrating remarkable performances in anomaly detection, anti-skid control performance, and safety improvement.

在飞机上，制动系统是起落架上一个对安全至关重要且使用频繁的部件，容易发生严重磨损。磨损动态中出现的异常会降低制动系统的性能，并危及地面操纵的安全性。在这项工作中，我们以与制动控制单元（BCU）紧密耦合的方式，解决了飞机制动器中初期异常情况的检测问题。本文提出了两种互补方法。第一种是基于纵向飞机动力学设计的观察器架构，可返回与磨损过程相关的物理可解释输出，使我们能够在线改进制动性能。第二种是基于卷积自动编码器的端到端架构，该架构根据 BCU 收集的数据计算异常得分，对模型不确定性具有固有的鲁棒性，而基于模型的架构则不具备这种鲁棒性。我们还提出了一种组合架构，可同时利用基于模型的方法和基于学习的方法的特点，从而显示出将这两种方法进行优化组合的能力。这些方法在 MATLAB/Simulink 多体仿真环境中进行了评估，该环境能够复制制动器磨损动态，在异常检测、防滑控制性能和安全性改进方面表现出色。

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引用次数: 0

Picture fuzzy complex proportional assessment approach with step-wise weight assessment ratio analysis and criteria importance through intercriteria correlation 图片模糊复合比例评估法与分步权重评估比率分析以及通过标准间相关性确定标准重要性

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109554

The concept of the picture fuzzy set (PiFS) significantly enhances the multi-criteria decision-making (MCDM) process by incorporating membership value (MV), non-membership value (NMV), and a neutral component. PiFS extends the capabilities of traditional fuzzy sets (FSs), intuitionistic fuzzy sets (IFSs), and other fuzzy models. This paper introduces a novel MCDM approach, the picture fuzzy SWARA-CRITIC-COPRAS (PiF-SCC) method, specifically designed to assist decision-makers (DMs) in evaluating and selecting dynamic digital marketing (DDM) technologies within PiFS settings. The proposed method integrates the strengths of PiFS with step-wise weight assessment ratio analysis (SWARA), criteria importance through intercriteria correlation (CRITIC), and complex proportional assessment (COPRAS), aiming to improve the precision and effectiveness of technology evaluations. To validate the approach, a case study is conducted on DDM technology assessment within a specific business context. The PiF-SCC technique is applied to rank technological options using linguistic terms (LTs), PiFS numbers, an accuracy function (AF), and a score function (SF). Additionally, a comprehensive sensitivity analysis is performed to evaluate the robustness of the proposed method under different input scenarios and uncertainties. A thorough comparison with existing techniques is also provided, demonstrating the superior decision-making capability of the new approach, which leads to more accurate and dependable technology selection results. The manuscript also discusses marginal implications and limitations, along with potential future research directions to further enhance the applicability and effectiveness of the proposed approach.

图片模糊集（PiFS）的概念通过整合成员值（MV）、非成员值（NMV）和中性成分，大大增强了多标准决策（MCDM）过程。PiFS 扩展了传统模糊集（FS）、直觉模糊集（IFS）和其他模糊模型的功能。本文介绍了一种新颖的 MCDM 方法，即图片模糊 SWARA-CRITIC-COPRAS (PiF-SCC) 方法，专门用于帮助决策者（DMs）在 PiFS 环境中评估和选择动态数字营销（DDM）技术。所提出的方法将 PiFS 的优势与逐步权重评估比率分析 (SWARA)、通过标准间相关性确定标准重要性 (CRITIC) 和复杂比例评估 (COPRAS) 相结合，旨在提高技术评估的精确性和有效性。为了验证该方法，我们在一个特定的业务环境中对 DDM 技术评估进行了案例研究。采用 PiF-SCC 技术，使用语言术语 (LT)、PiFS 数字、准确度函数 (AF) 和评分函数 (SF) 对技术方案进行排序。此外，还进行了全面的敏感性分析，以评估拟议方法在不同输入情景和不确定性下的稳健性。还提供了与现有技术的全面比较，证明新方法具有卓越的决策能力，能带来更准确、更可靠的技术选择结果。手稿还讨论了边际影响和局限性，以及未来可能的研究方向，以进一步提高拟议方法的适用性和有效性。

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引用次数: 0

Investigation of hybrid modeling and its transferability in building load prediction used for district heating systems 混合建模及其在区域供热系统建筑负荷预测中的可移植性研究

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109544

In the district heating systems, the historical operation data of the buildings in those areas would be partially or entirely missing. The traditional data-driven model is hard to predict the ground truth results because the historical data is not available for model training. However, utilizing the physics-based methods for load calculation takes a long time to process and encounters low accuracy issues. This paper investigates several hybrid models that integrate the data-driven model and the physics-based models with different fusion methods. The physics-based models calculate envelope load and infiltration load, based on Fourier's law and the grand canonical ensemble theory, respectively. After undergoing load processing, features fusion, and residual connection, the best advanced hybrid models generate 21.35%, 16.35%, and 12.73% better prediction results compared with the data-driven model. Moreover, the advanced hybride models also perform strong transferability across all the data quantity groups. In terms of practical application, the advanced hybrid models could be deployed with effective generalization in limited data scenarios and robust transfer capabilities. The selected best model constructed by hybrid modeling displays the highest performance and saves the total training costs with strong transferability.

在区域供热系统中，这些区域内建筑物的历史运行数据可能部分或全部缺失。传统的数据驱动模型很难预测真实结果，因为没有历史数据用于模型训练。然而，利用基于物理的方法进行负荷计算需要较长的处理时间，并且会遇到精度较低的问题。本文研究了几种将数据驱动模型和基于物理的模型与不同的融合方法相结合的混合模型。基于物理的模型分别根据傅立叶定律和大规范集合理论计算围护荷载和渗透荷载。经过负荷处理、特征融合和残差连接后，最佳的高级混合模型与数据驱动模型相比，预测结果分别提高了 21.35%、16.35% 和 12.73%。此外，高级混合模型在所有数据量组之间也具有很强的可移植性。在实际应用方面，先进的混合模型可以在有限的数据场景中有效泛化，并具有强大的转移能力。通过混合建模构建的最佳模型性能最高，并能节省总的训练成本，具有很强的可移植性。

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引用次数: 0

A deep learning ensemble approach for malware detection in Internet of Things utilizing Explainable Artificial Intelligence 利用可解释人工智能的深度学习集合方法检测物联网中的恶意软件

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109560

The Internet of Things (IoT) has been popularized these days due to digitization and automation. It is deployed in various applications, i.e., smart homes, smart agriculture, smart transportation, smart healthcare, and industrial monitoring. In an IoT network, many IoT devices communicate with servers, or users access IoT devices through an open channel via a certain exchange of messages. Besides providing many benefits like efficiency, automation, and convenience, IoT presents significant security challenges due to a lack of proper standard security measures. Thus, malicious actors may be able to infect the network with malware. They may launch destructive attacks with the goal of stealing data or causing damage to the systems’ resources. This can be mitigated by introducing intrusion detection and prevention mechanisms in the network. An intelligent intrusion detection system is required to put preventative measures in place for secure communication and a malware-free network. In this article, we propose a deep learning based ensemble approach for IoT malware attack detection (in short, we call it as DLEX-IMD) trained and tested against benchmark datasets. The important measures, including accuracy, precision, recall, and F1-score, are used to evaluate the performance of the proposed DLEX-IMD. The performance of the proposed scheme is explained utilizing benchmark Explainable Artificial Intelligence (AI) method–LIME (Local Interpretable Model-Agnostic Explanations), which justifies the reliability of the proposed model training. The DLEX-IMD is also compared with a range of other closely related existing schemes and has shown better performance than those schemes with 99.96% accuracy and F1-score of 0.999.

由于数字化和自动化的发展，物联网（IoT）如今已得到普及。它被部署在各种应用中，如智能家居、智能农业、智能交通、智能医疗和工业监控。在物联网网络中，许多物联网设备与服务器进行通信，或者用户通过一定的信息交换，以开放的渠道访问物联网设备。除了提供效率、自动化和便利等诸多好处外，由于缺乏适当的标准安全措施，物联网还带来了巨大的安全挑战。因此，恶意行为者可能会用恶意软件感染网络。他们可能会发起破坏性攻击，目的是窃取数据或破坏系统资源。在网络中引入入侵检测和预防机制可以缓解这种情况。为了实现安全通信和无恶意软件网络，需要一个智能入侵检测系统来采取预防措施。在本文中，我们提出了一种基于深度学习的物联网恶意软件攻击检测集合方法（简而言之，我们称之为 DLEX-IMD），并根据基准数据集进行了训练和测试。我们使用准确率、精确度、召回率和 F1 分数等重要指标来评估所提出的 DLEX-IMD 的性能。利用基准可解释人工智能（AI）方法--LIME（本地可解释模型-诊断解释）解释了所提方案的性能，证明了所提模型训练的可靠性。我们还将 DLEX-IMD 与其他一系列密切相关的现有方案进行了比较，结果表明 DLEX-IMD 的准确率为 99.96%，F1 分数为 0.999，性能优于这些方案。

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引用次数: 0

TopoPIS: Topology-constrained pipe instance segmentation via adaptive curvature convolution TopoPIS：通过自适应曲率卷积进行拓扑约束管道实例分割

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence

Pub Date : 2024-11-04 DOI: 10.1016/j.engappai.2024.109547

Precise and fast pipe instance segmentation is a critical component in industrial automatic assembly, facilitating accurate object detection and pose estimation, optimizing and supervising the assembly process. However, this problem is challenging due to topological errors on fine-scale structures caused by the pipes being complex and slender. To address these challenges, we propose a topology-constrained pipe instance segmentation network (TopoPIS) for complex stacking scene to achieve accurate segmentation with topological correctness. To better extract the features of complex and variable morphological pipes, adaptive curvature convolution is introduced to dynamically adapt to the slender pipe structure and capture critical features. To handle topological errors like broken connections, we propose a novel topological constraint loss function based on persistent homology, which greatly improves the topological continuity of the segmentation. Experimental results on real-world and unseen datasets demonstrate that our TopoPIS outperforms other methods regrading segmentation accuracy and topological continuity.

精确而快速的管道实例分割是工业自动装配中的一个关键组成部分，有助于准确的对象检测和姿态估计，优化和监督装配过程。然而，由于管道复杂而纤细，在精细结构上存在拓扑误差，因此这一问题具有挑战性。为了应对这些挑战，我们提出了一种针对复杂堆叠场景的拓扑约束管道实例分割网络（TopoPIS），以实现具有拓扑正确性的精确分割。为了更好地提取形态复杂多变的管道特征，我们引入了自适应曲率卷积，以动态适应细长的管道结构并捕捉关键特征。为了处理断开连接等拓扑错误，我们提出了一种基于持久同源性的新型拓扑约束损失函数，大大提高了分割的拓扑连续性。在真实世界和未知数据集上的实验结果表明，在分割准确性和拓扑连续性方面，我们的 TopoPIS 优于其他方法。

引用次数: 0