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computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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Co‑packaged optics(CPO):status,challenges,and solutions

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Frontiers of Optoelectronics 2023年第1期16卷 1-40页

作者： Min Tan Jiang Xu Siyang Liu Junbo Feng Hua Zhang Chaonan Yao Shixi Chen Hangyu Guo Gengshi Han Zhanhao Wen Bao Chen Yu He Xuqiang Zheng Da Ming Yaowen Tu Qiang Fu Nan Qi Dan Li Li Geng Song Wen Fenghe Yang Huimin He Fengman Liu Haiyun Xue Yuhang Wang Ciyuan Qiu Guangcan Mi Yanbo Li Tianhai Chang Mingche Lai Luo Zhang Qinfen Hao Mengyuan Qin School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong KongChina HKUST Fok Ying Tung Research Institute Guangzhou 511462China The Hong Kong University of Science and Technology(Guangzhou) Guangzhou 511462China Chongqing United Micro-Electronics Center(CUMEC) Chongqing 401332China Hisense Broadband Multimedia Technologies Co. Ltd.Qingdao 266000China Institute of Microelectronics Chinese Academy of SciencesBeijing 100029China State Key Laboratory of Superlattices and Microstructures Institute of SemiconductorsChinese Academy of SciencesBeijing 100083China School of Microelectronics Xi’an Jiaotong UniversityXi’an 710049China Zhangjiang Laboratory Shanghai 201210China The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Huawei Technologies Co. Ltd.Shenzhen 440307China College of Computer National University of Defense TechnologyChangsha 410073China Institute of Computing Technology Chinese Academy of SciencesBeijing 100086China

Due to the rise of 5G,IoT,AI,and high-performance computing applications,datacenter trafc has grown at a compound annual growth rate of nearly 30%.Furthermore,nearly three-fourths of the datacenter trafc resides within *** conventional pluggable optics increases at a much slower rate than that of datacenter *** gap between application requirements and the capability of conventional pluggable optics keeps increasing,a trend that is *** optics(CPO)is a disruptive approach to increasing the interconnecting bandwidth density and energy efciency by dramatically shortening the electrical link length through advanced packaging and co-optimization of electronics and *** is widely regarded as a promising solution for future datacenter interconnections,and silicon platform is the most promising platform for large-scale *** international companies(e.g.,Intel,Broadcom and IBM)have heavily investigated in CPO technology,an inter-disciplinary research feld that involves photonic devices,integrated circuits design,packaging,photonic device modeling,electronic-photonic co-simulation,applications,and *** review aims to provide the readers a comprehensive overview of the state-of-the-art progress of CPO in silicon platform,identify the key challenges,and point out the potential solutions,hoping to encourage collaboration between diferent research felds to accelerate the development of CPO technology.

关键词： Co-packaged optics Silicon photonics High-performance computing Advanced packaging External laser Optical power delivery Co-simulation Standardization Transmitter Receiver

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Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation

arXiv

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arXiv 2023年

作者： Yang, Jingwei Xue, Bohuan Feng, Yi Wang, Deming Fan, Rui Chen, Qijun The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong

This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs). 3F2N+ achieves this capability by utilizing a novel discontinuity discrimination module (DDM), which combines depth curvature minimization and correlation coefficient maximization through conditional random fields (CRFs). To evaluate the robustness of SNEs on noisy data, we create a large-scale synthetic surface normal (SSN) dataset containing 20 scenarios (ten indoor scenarios and ten outdoor scenarios with and without random Gaussian noise added to depth images). Extensive experiments demonstrate that 3F2N+ achieves greater performance than all other geometry-based surface normal estimators, with average angular errors of 7.85◦, 8.95◦, 9.25◦, and 11.98◦ on the clean-indoor, clean-outdoor, noisy-indoor, and noisy-outdoor datasets, respectively. We conduct three additional experiments to demonstrate the effectiveness of incorporating our proposed 3F2N+ into downstream robot perception tasks, including freespace detection, 6D object pose estimation, and point cloud completion. Our source code and datasets are publicly available at https://***/3F2Nplus. Note to Practitioners—The primary motivation behind this work arises from the need to develop a high-performing surface normal estimator for practical robotics and computer vision applications. While geometry-based surface normal estimators have been widely used in these domains, the existing solutions focus merely on discontinuity discrimination. To tackle this problem, this article introduces a plug-and-play module that leverages both depth curvature and correlation coefficient to quantify discontinuity levels, thereby optimizing surface normal estimation, particularly near or on discontinuous regions. Moreover, this article also introduces a large-scale public dataset wit

关键词： Gaussian noise (electronic)

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Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

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Environmental science and Ecotechnology 2024年第3期19卷 100-108页

作者： Bo Xu Haofei Yu Zongbo Shi Jinxing Liu Yuting Wei Zhongcheng Zhang Yanqi Huangfu Han Xu Yue Li Linlin Zhang Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFLUSA School of Geography Earth and Environment Sciences University of BirminghamBirminghamB152TTUK State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Key Laboratory of air Pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin300072China Gigantic Technology(Tianjin)Co. LtdTianjin300072China College of Computer Science Nankai UniversityTianjin300350China China National Environmental Monitoring Centre Beijing100012China

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion *** process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning ***,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient *** urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory *** approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual ***,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest *** research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

关键词： Machine learning Data driven Theoretical approach Domain knowledge Guide

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Adaptive Soft Pneumatic Robot Inspired by Locomotion of Snake

Adaptive Soft Pneumatic Robot Inspired by Locomotion of Snak...

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IEEE International Conference on Robotics and Biomimetics

作者： Jinxuan Lu Xinshu Hu Xiaoyu Zhu Cheng Chen Hongqiang Wang Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Pneumatic soft robots have garnered widespread attention for their flexibility and adaptability in various applications. However, existing soft robots generally struggle to effectively navigate diverse terrains, especially when it comes to climbing obstacles. This paper introduces an adaptive pneumatic soft robot designed based on snake-like locomotion, which effectively addresses this issue. The robot utilizes four corrugated tubes as its soft actuators, complemented by silicone-made feet with bidirectional friction anisotropy, enabling it to adapt to various terrains and exhibit climbing capabilities. Experiments demonstrate that the robot can traverse different surfaces such as sand, gravel, and slopes, and can climb obstacles up to 20 centimeters high. Additionally, it features a self-regulation mechanism that allows it to recover from accidental overturns. This study presents a soft robot design with multi-terrain adaptability and self-recovery capabilities.

关键词： Corrugated surfaces Actuators Anisotropic magnetoresistance Navigation Friction Biomimetics MIMICs Soft robotics Pneumatic systems Environmental monitoring

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Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis:Convergence and Challenges

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Precision Chemistry 2024年第10期2卷 518-538页

作者： Yuxin Yang Yueqi Li Longhua Tang Jinghong Li State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Nanhu Brain-Computer Interface Institute HangzhouZhejiang 311100China Center for BioAnalytical Chemistry Hefei National Laboratory of Physical Science at MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Department of Chemistry Center for BioAnalytical ChemistryKey Laboratory of Bioorganic Phosphorus Chemistry&Chemical BiologyTsinghua UniversityBeijing 100084China Beijing Institute of Life Science and Technology Beijing 102206ChinaNew Cornerstone Science InstituteBeijing 102206China

Single-molecule bioelectronic sensing,a groundbreaking domain in biological research,has revolutionized our understanding of molecules by revealing deep insights into fundamental biological *** advent of emergent technologies,such as nanogapped electrodes and nanopores,has greatly enhanced this field,providing exceptional sensitivity,resolution,and integration ***,challenges persist,such as complex data sets with high noise levels and stochastic molecular *** intelligence(AI)has stepped in to address these issues with its powerful data processing *** algorithms effectively extract meaningful features,detect subtle changes,improve signal-to-noise ratios,and uncover hidden patterns in massive *** review explores the synergy between AI and single-molecule bioelectronic sensing,focusing on how AI enhances signal processing and data analysis to boost accuracy and *** also discuss current limitations and future directions for integrating AI,highlighting its potential to advance biological research and technological innovation.

关键词： single-molecule bioelectronic sensing artificial intelligence machine learning signal processing biosensors

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Data Availability Optimization for Cyber-Physical systems 15

Data Availability Optimization for Cyber-Physical Systems

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Li, Liying Cong, Peijin Zhou, Junlong Gu, Zonghua Li, Keqin School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Applied Physics and Electronics Ume˚a University Sweden Department of Computer Science State University of New York United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the backbone of Industry 4.0, Cyber-Physical systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. The appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. The proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%. © 2022 IEEE.

关键词： Iterative methods

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BD-WNet: Boundary Decoupling based W-shape Network for Road Segmentation in Optical Remote Sensing Imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Fan, Shilong Xie, Jianlin Zhuang, Yin Chen, He Su, Zhibao Li, Lianlin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing100072 China Peking University Schools of Computer Science and State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China

Using very high-resolution optical remote sensing images for road segmentation is a challenging and important interpretation task. Different from other segmentation tasks, road segmentation typically faces unpredictable structure, irregular distribution, and complex background interference. Thus, establishing an effective and stable semantic description for road segmentation becomes a challenge. In this article, a novel architecture called Boundary Decoupling based W-shape Network (BD-WNet) is proposed for achieving road segmentation from optical remote sensing imagery. First, a novel W-shaped double encoder-decoder architecture network is designed to provide more stable semantic description, which can be used for road body extraction. Second, the unstable semantic features within the initial stage of double encoder-decoder architecture are considered for road boundary description. For decoupling unstable boundary information from the output feature of first encoder-decoder, a boundary separate model is designed, which is called Boundary-Body Decoupling (BBD) module. This module utilizes the flow field mechanism to compare image features before and after passing through the encoder-decoder. The stable features among the overall features are decoupled into the main body of the road, while the dynamic features are decoupled into the boundary of the road. Third, a boundary and body weighting fusion model is also designed to fuse stable road body and unstable road boundary information for supervised learning. Extensive experiments have been carried out on remote sensing road segmentation datasets, and our method achieves impressive performance. Specifically, the proposed BD-WNet achieves 82.5% F1 score and 69.8 IoU% on DeepGlobe dataset, 75.5% F1 score and 60.7% IoU on CHN6-CUG dataset and 93.8% F1 score and 88.3% IoU on Ottawa Road Dataset. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Efficient and stable perovskite solar cells by build-inπ-columns and ionic interfaces in covalent organic frameworks

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Nano Research 2023年第7期16卷 9387-9397页

作者： Riming Nie Xiaokai Chen Zhongping Li Weicun Chu Si Ma Changqing Li Xiaoming Liu Yonghua Chen Zhuhua Zhang Wanlin Guo State Key Laboratory of Mechanics and Control of Mechanical Structures Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of EducationInstitute for Frontier ScienceNanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Chemical and Biomolecular Engineering Yonsei University50 Yonsei-roSeodaemun-guSeoul 120-749Republic of Korea College of Chemistry Jilin UniversityChangchun 130012China Department of Energy and Chemical Engineering Ulsan National Institute of Science and Technology(UNIST)50 UNIST-gilEonyang-eupUljugunUlsan 44919Republic of Korea Key Laboratory of Flexible Electronics(KLoFE) School of Flexible Electronics(Future Technologies)&Institute of Advanced Materials(IAM)Nanjing Tech University(Nanjing Tech)Nanjing 211816China

Perovskite solar cells(PSCs)have attracted much attention due to their rapidly increased power conversion efficiencies,however,their inherent poor long-term stability hinders their *** degradation of PSCs first comes from the degradation of hole transport materials(HTMs).Here,we report the construction of periodicπ-columnar arrays and ionic interfaces over the skeletons by introducing cationic covalent organic frameworks(C-COFs)to the ***π-columnar arrays can optimize the charge transport ability and energy levels of the hole transport layer and suppress the degradation of HTM,and ionic interfaces over the skeletons can produce stronger electric dipole and electrostatic interactions,as well as higher charge *** C-COFs were designed and synthesized via Schiff base reaction by using 1,3,5-triformylphloroglucinol as a neutral knot and dimidium bromide as cationic *** neutral COFs(N-COFs)were also synthesized as a reference by using 3,8-diamino-6-phenylphenanthridine as neutral *** with cationic COF exhibit the highest efficiency of 23.4%with excellent humidity and thermal *** the best of our knowledge,this is the highest efficiency among the meso-structured PSCs fabricated by a sequential process.

关键词： cationic covalent organic framework(C-COF) π-columnar arrays ionic interfaces charge density perovskite solar cells

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Memristor-Based Circuit of Sequential Associative Memory with Memory Interactions and Stimulus Similarity Effect

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Xiong, Dongdong Wang, Xiaoping Jiang, Yiming Yang, Chao Jiang, Man Lai, Jingang Zeng, Zhigang Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Braininspired Intelligent Systems Wuhan430074 China Huazhong University of Science and Technology Institute of Artificial Intelligence School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Huazhong University of Science and Technology Department of Physiology School of Basic Medicine and Tongji Medical College Wuhan430030 China

Most proposed memristor-based circuits of associative memory consider various mechanisms in only one associative memory. Few works on circuit design of sequential associative memory have been reported. In this paper, a memristor-based circuit of sequential associative memory with memory interactions and stimulus similarity effect is designed. Two associative memories, the prior associative memory (PAM) and the later associative memory (LAM), are formed successively. The PAM modifies the rate of the formation of the LAM, and the LAM, in turn, affects the strength of the PAM, which are two interactions in sequential associative memory, called transfer and retroaction. In addition, the magnitudes of transfer and retroaction are determined by the similarity of the conditioned stimuli. The above functions are realized by the ring input processing module, memory module, transfer module and retroaction module. It has been identified in circuit analysis that the proposed circuit is power efficient and has good robustness. Furthermore, the proposed circuit has promising applications for fire rescue robots. © 2016 IEEE.

关键词： Associative storage

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