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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Anomaly detection of EMU trains based on line-scan image registration 7

Anomaly detection of EMU trains based on line-scan image reg...

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7th Global intelligent Industry Conference, GIIC 2024

作者： Fang, Lei Shi, Zelin Xiao, Chuanmin Liu, Yunpeng Pang, Mingqi Zhao, Enbo Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Third Military Representative Room in Shenyang Area Air Force Equipment Shenyang110016 China

ISBN: (纸本)9781510682986

Anomaly detection based on template image registration is one of the methods used to detect anomalies of objects with similar structures. However, the imaging device of the Electronic Multiple Units (EMU) train is a line-scan camera, and the geometric transformation law between its image and the area-scan image is different. The conventional image registration method of the area-scan image cannot accurately align the line-scan image. Moreover, the line-scan image of the EMU train collected in uncontrollable environmental scenes exhibits significant grayscale changes, and the registered images cannot detect differences by directly comparing grayscale. To address these two challenges, this paper proposes a two-stage anomaly detection method based on line-scan image registration and edge comparison. In the registration stage, a coarse-to-fine line-scan image registration method was designed. First, the feature-based registration method was used to achieve coarse position of the EMU train template image in the target image. Then, the direct registration method based on the line-scan image geometric transformation model achieved precise geometric alignment. In the anomaly detection stage, edge information is extracted from the template image and the registered target image, and anomaly detection of EMU trains is achieved through edge comparison, edge expansion, and edge connection. The experimental results on the line-scan image of EMU trains show that the two-stage method proposed in this paper can achieve the registration of line-scan images of EMU trains, and on this basis, achieve detection and segmentation of abnormal areas of EMU trains. © 2024 SPIE.

关键词： Image registration

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The Simulation of the Terahertz Modulator by CMOS Process 3

The Simulation of the Terahertz Modulator by CMOS Process

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3rd International Conference on Defence Technology, ICDT 2022

作者： Zhang, Chenyu Hu, Nairui Liu, Zhaoyang School of Electronic Information Engineering Shenyang Aerospace University Liaoning Province Shenyang110136 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The paper introduced the simulation of the terahertz modulator in complementary metal-oxide-semiconductor (CMOS) process. The modulator is composed of a metal split-ring resonator (SRR), CMOS, semiconductor dielectric layer and silicon substrate. The modulator can make different electromagnetic response to the transmitted terahertz wave between the connection state and the disconnection state of the gap in the SRR, which could be achieved by connecting CMOS in the gap. At 0.31THz, the simulation results show that the amplitude modulation depth of the modulator reached 28.8%. When the simulation keeped the modulator system in resonating state, the transmission coefficient was about 0.0018, while the conductive had reached 0.2895. If the design can pass the experimental verification in the future, it can make some references for further exploration of the high-speed and high modulation depth of the terahertz amplitude modulator. © Published under licence by IOP Publishing Ltd.

关键词： Substrates

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PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION FOR UNSUPERVISED DOMAIN ADAPTATION

PROGRESSIVE DISTRIBUTION ALIGNMENT BASED ON LABEL CORRECTION...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Yong Li, Desheng Lu, Yuwu Gao, Can Wang, Wenjing Lu, Jianglin College of Computer Science and Software Engineering Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781665438643

Unsupervised domain adaptation (UDA) aims to transfer knowledge between different domains. Most of the existing UDA methods try to align the conditional distribution between the source and target domains by utilizing the information of pseudo labels induced from the target domain. To tackle the negative transfer caused by inaccurate pseudo labels, we propose a novel UDA method named progressive distribution alignment based on label correction (PDALC). Specifically, PDALC uses the class discriminative information to perform subspace learning to obtain the domain invariance subspace. Furthermore, a new mechanism of pseudo label correction is introduced to measure the reliability of pseudo labels and utmostly correct the inaccurate pseudo labels. By combining subspace learning with label correction, the performance of PDALC can be continuously improved, which in turn reduces the generation of inaccurate pseudo labels. The experimental results show that the proposed method outperforms the state-of-the-art UDA methods. © 2021 IEEE

关键词： Computer vision

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Shift from Texture-bias to Shape-bias: Edge Deformation-based Augmentation for Robust Object Recognition

Shift from Texture-bias to Shape-bias: Edge Deformation-base...

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International Conference on Computer Vision (ICCV)

作者： Xilin He Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Feng Liu Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institue of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing University of Leicester

Recent studies have shown the vulnerability of CNNs under perturbation noises, which is partially caused by the reason that the well-trained CNNs are too biased toward the object texture, i.e., they make predictions mainly based on texture cues. To reduce this texture-bias, current studies resort to learning augmented samples with heavily perturbed texture to make networks be more biased toward relatively stable shape cues. However, such methods usually fail to achieve real shape-biased networks due to the insufficient diversity of the shape cues. In this paper, we propose to augment the training dataset by generating semantically meaningful shapes and samples, via a shape deformation-based online augmentation, namely as SDbOA. The samples generated by our SDbOA have two main merits. First, the augmented samples with more diverse shape variations enable networks to learn the shape cues more elaborately, which encourages the network to be shape-biased. Second, semantic-meaningful shape-augmentation samples could be produced by jointly regularizing the generator with object texture and edge-guidance soft constraint, where the edges are represented more robustly with a self information guided map to better against the noises on them. Extensive experiments under various perturbation noises demonstrate the obvious superiority of our shape-bias-motivated model over the state of the arts in terms of robustness performance. Code is available at https://***/C0notSilly/-ICCV-23-Edge-Deformation-based-Online-Augmentation.

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Sea Scene Classification from Synthetic Aperture Radar Images Using A modified MobileNetV1 3

Sea Scene Classification from Synthetic Aperture Radar Image...

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3rd International Conference on Signal Image processing and Communication, ICSIPC 2023

作者： Wang, Zhongbo He, Miao Ding, Qinghai Luo, Haibo Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Space Star Technology Co Ltd. Beijing100086 China

ISBN: (纸本)9781510670945

Synthetic aperture radar (SAR) has a special ability to work in any type of inclement weather, and is a very suitable tool for Ocean surveillance. Scene classification is an essential pre-task of other computer vision tasks for ocean monitoring. It is of great importance to develop scene classification technology of SAR sea images. Due to the excellent feature representation abilities of neural networks, the deep learning-based methods are far superior to the traditional methods based on manual features in scene classification task performance. Many lightweight classification networks have been proposed to improve the inference speed of the networks. But in comparison with ordinary CNNs, the lightweight networks have slightly lower accuracy for scene classification tasks. So in this article, we proposed an improved lightweight Convolutional Neural Network for scene classification of SAR sea images. First, in order to meet the real-time performance, we choose MobileNetv1 as the original classification network in this paper. Then, to compensate for the lack of accuracy, we use 1D asymmetric convolution kernels to strengthen each layer of the depthwise convolutions in the network. Finally, after training time, we merge the linear calculations of each layer of the network to convert it into the original structure. The experimental results show that the modified model has obtained an accuracy improvement than the original one on the scene classification of sea SAR images without extra computation. © 2023 SPIE.

关键词： Synthetic aperture radar

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Fast Relocalization and Loop Closing in Keyframe-Based 3D LiDAR SLAM

Fast Relocalization and Loop Closing in Keyframe-Based 3D Li...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Cui, Yunge Wu, Qingxiao Hao, Yingming Kong, Yanzi Lin, Zhiyuan Zhu, Feng Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665481090

Relocalization and Loop closing play important roles in robotic simultaneous localization and mapping (SLAM). In this work, we propose a light-weight bag of words (BoW) method for the relocalization and loop closing in 3D LiDAR SLAM, which is based on BoW3D. Different from original BoW3D, to further improve the retrieval efficiency, we use the words of keyframes to build the vocabulary of the database. Based on the light-weight database, we propose a relocalization method to deal with the tracking failure cases, and also propose a keyframe-based loop closing method to improve the accuracy of 3D LiDAR SLAM. In experiment, we test the recognition performance of keyframe-based database, and evaluate the relocalization performance. To evaluate the loop closing performance in practice, we furthermore embed the keyframe-based BoW3D to LiDAR odometry system. The experimental results show that the proposed place recognition method not only achieves competitive retrieval performance but also shows great recognition efficiency. © 2022 IEEE.

关键词： Database systems

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Distributed Autonomous Swarm Formation for Dynamic Network Bridging

Distributed Autonomous Swarm Formation for Dynamic Network B...

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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Raffaele Galliera Thies Mohlenhof Alessandro Amato Daniel Duran Kristen Brent Venable Niranjan Suri Institute for Human & Machine Cognition (IHMC) Department of Intelligent Systems & Robotics The University of West Florida (UWF) Pensacola FL USA Fraunhofer Institute for Communication Information Processing and Ergonomics (FKIE) Wachtberg Germany US Army Research Laboratory (ARL) Adelphi MD USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Effective operation and seamless cooperation of robotic systems are a fundamental component of next-generation technologies and applications. In contexts such as disaster response, swarm operations require coordinated behavior and mobility control to be handled in a distributed manner, with the quality of the agents' actions heavily relying on the communication between them and the underlying network. In this paper, we formulate the problem of dynamic network bridging in a novel Decentralized Partially Observable Markov Decision Process (Dec-POMDP), where a swarm of agents cooperates to form a link between two distant moving targets. Furthermore, we propose a Multi-Agent Reinforcement Learning (MARL) approach for the problem based on Graph Convolutional Reinforcement Learning (DGN) which naturally applies to the networked, distributed nature of the task. The proposed method is evaluated in a simulated environment and compared to a centralized heuristic baseline showing promising results. Moreover, a further step in the direction of sim-to-real transfer is presented, by additionally evaluating the proposed approach in a near Live Virtual Constructive (LVC) UAV framework.

关键词： Training Robot kinematics Markov decision processes Disasters Conferences Reinforcement learning Autonomous aerial vehicles

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Refined Infrared Small Target Detection Scheme with Single-Point Supervision

arXiv

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arXiv 2024年

作者： Zhao, Jinmiao Shi, Zelin Yu, Chuang Liu, Yunpeng Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Recently, infrared small target detection with single-point supervision has attracted extensive attention. However, the detection accuracy of existing methods has difficulty meeting actual needs. Therefore, we propose an innovative refined infrared small target detection scheme with single-point supervision, which has excellent segmentation accuracy and detection rate. Specifically, we introduce label evolution with single point supervision (LESPS) framework and explore the performance of various excellent infrared small target detection networks based on this framework. Meanwhile, to improve the comprehensive performance, we construct a complete post-processing strategy. On the one hand, to improve the segmentation accuracy, we use a combination of test-time augmentation (TTA) and conditional random field (CRF) for post-processing. On the other hand, to improve the detection rate, we introduce an adjustable sensitivity (AS) strategy for post-processing, which fully considers the advantages of multiple detection results and reasonably adds some areas with low confidence to the fine segmentation image in the form of centroid points. In addition, to further improve the performance and explore the characteristics of this task, on the one hand, we construct and find that a multi-stage loss is helpful for fine-grained detection. On the other hand, we find that a reasonable sliding window cropping strategy for test samples has better performance for actual multi-size samples. Extensive experimental results show that the proposed scheme achieves state-of-the-art (SOTA) performance. Notably, the proposed scheme won the third place in the "ICPR 2024 Resource-Limited Infrared Small Target Detection Challenge Track 1: Weakly Supervised Infrared Small Target Detection". Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection 22

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Fan Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei 230026 China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou 215123 China Key Laboratory of Precision and Intelligent Chemistry Ustc Anhui Hefei 230026 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Cas Beijing 100190 China

ISBN: (纸本)9798331520526

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment. © 2025 IEEE.

关键词： Anatomical Landmark Detection Unsupervised Domain Adaptation

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