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International Joint Conference on Neural Networks (IJCNN)

作者： Lu Yang Kai Zhang Jiaqi Liu Chongke Bi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China Autobrain (Tianjin) Technology LTD Tianjin China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the field of object detection bounding box regression, IoU (Intersection over Union) is a commonly used evaluation metric for measuring the overlap between predicted and ground truth bounding boxes. One limitation of IoU, however, is that the gradient goes to zero when there is no intersection between bounding boxes. Recent evaluation metrics have been developed to overcome this problem by incorporating penalty terms and other techniques for improving IoU. Despite these advances, existing methods still suffer to some degree from the problems of small gradients and slow rate of convergence. In this paper, a new evaluation method named LIoU (Location IoU) is proposed to address these issues. It can maximize the shared area of the intersection when used as the loss function and incorporate a variable gradient parameter to fit to datasets of different sizes. The experimental results indicate that we achieve 0.27% and 3.48% accuracy improvements respectively in SSD and YOLOv5 network on the VOC dataset, and 2.23% in the YOLOv5 network on the COCO dataset. The results of this study highlight the effectiveness of LIoU in improving the performance of object detection models.

关键词： YOLO Measurement Location awareness Accuracy Shape Neural networks Benchmark testing

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RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

arXiv

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arXiv 2023年

作者： Liu, Jiuming Wang, Guangming Liu, Zhe Jiang, Chaokang Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China China University of Mining and Technology China ETH Zürich Switzerland Microsoft United States

Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves competitive performance in terms of both accuracy and efficiency. Codes are available at https://***/IRMVLab/RegFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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A framework for human-exoskeleton interaction based on sEMG interface and electrotactile feedback

A framework for human-exoskeleton interaction based on sEMG ...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Duan, Shengcai Wang, Can Li, Mengyao Su, Zhilong Liu, Jiaqing Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences Department of Mechanical and Automation Engineering Chinese University of Hong Kong

ISBN: (纸本)9781665436786

The lower extremity rehabilitation exoskeleton provides a priceless opportunity for paraplegics to stand up and walk normally. In this paper, a framework for human-exoskeleton interaction with sEMG interface and electrotactile feedback is presented. Firstly, the exoskeleton recognises human motion intention through the Long Short Term Memory (LSTM) Neural Network based on surface electromyogram (sEMG). Secondly, electrotactile based on spatiotemporal coding scheme feeds back the exoskeleton states to the sensory skin, as a compensation for paraplegic's absent proprioception in lower limbs. Thirdly, physiological information, i.e. muscle fatigue, of arms is monitored and quantified with sEMG. Three healthy subjects participate the experiments including the motion intention recognition experiment, electrotactile perception experiment and online exoskeleton experiment. The online exoskeleton experiments demonstrate the feasibility and efficiency of the proposed framework. Furthermore, it is proved experimentally that the performance of collaborative control in rehabilitation human-exoskeleton interaction can be facilitated via the framework. © 2021 IEEE.

关键词： Long short-term memory

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Application of the High-Q Bouncing Ball Model Based on Fabry-Pérot Cavity for Alcohol Gas Concentration Detection

SSRN

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SSRN 2023年

作者： Sang, Luxiao Wang, Teng Zhao, Biao Ding, Jinyu Jian, Aoqun Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030031 China

This paper presents a method to enhance the Q factor of Fabry-Pérot cavities using a vertical dielectric interface (DI) made of silicon and air, which generates a bouncing ball model. The method is applied to detect alcohol gas concentrations with high resolution. Specifically, we introduce a 450 µm-thick silicon wafer into the Fabry-Pérot (FP) cavity, where it is positioned at an angle of inclination. This addition considerably augments the light-matter interaction, effectively confining the light within the cavity. The Q factor of the FP cavity with DI is always higher than that of the open FP cavity in the inclination angle range of 0 to 1°. Moreover, the effective mode area of the FP cavity with DI is smaller. Furthermore, the FP cavity with DI reduces the effective mode area and diminishes the lateral walk-off loss experienced in the open FP cavity. Finally, we experimentally verified the effectiveness of this method by detecting alcohol gas concentrations ranging from 28.5 ppm to 142.5 ppm and achieving a Q factor improvement from 5.9×104 to 1.2×105. The system sensitivity was calculated to be 0.5294 pm/ppm, and the resolution obtained was 28.5 ppm. © 2023, The Authors. All rights reserved.

关键词： Silicon wafers

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Vehicle detection based on point cloud intensity and distance clustering 5

Vehicle detection based on point cloud intensity and distanc...

引用

2020 5th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Wang, Zhao Wang, Xing Fang, Bin Yu, Kun Ma, Jie School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multispectral Information Processing Technology Huazhong University of Science and Technology Wuhan430074 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering. © Published under licence by IOP Publishing Ltd.

关键词： Optical radar

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Research on Robust Measurement Method of Heart Rate Using Remote Photoplethysmography Based on Adversarial Learning Network with High and Low Frequency Features

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Zhai, Dezhao Chen, Wei Ding, Yinghao Yu, Ming Li, Qinwei Wu, Hang Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China People's Liberation Army Systems Engineering Institute Academy of Military Sciences Tianjin300161 China Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin300300 China Nankai University School of Artificial Intelligence Tianjin300381 China

Remote Photoplethysmography (rPPG) is a non-contact method for measuring heart rate (HR) through facial video, breaking the constraints of contact measurements and offering broad application prospects. However, in real monitoring scenarios, the distance of subjects and facial illumination often vary. This leads to degradation of rPPG signals due to changes in facial resolution and light intensity. This paper presents the High and Low Frequency Feature Adversarial Learning Network (HLFF-AL), which utilizes feature space interpolation and composite feature capture to perform multi-band global and local signal extraction. By adopting an adversarial learning strategy, it enhances the capability to capture semantic information of rPPG under varying resolutions and lighting, bridging the differences in rPPG signal generation due to resolution changes. While maintaining accuracy in HR measuring, the robustness of the network is improved to achieve robust measurement of HR via rPPG. Even under high-resolution conditions, the Mean Absolute Error (MAE) for the three public datasets - unchanging lighting (COHFACE, UBFC-rPPG) and varying lighting (BUAA-MIHR) - reached 1.10 bpm, 1.64 bpm, and 4.41 bpm, respectively, with a mere 0.29 bpm difference in MAE between high and low resolutions on the BUAA-MIHR dataset. This demonstrates that HLFF-AL can predict more robust rPPG signals in various resolutions and lighting scenarios, achieving competitive results compared to state-of-the-art methods. © 1991-2012 IEEE.

关键词： Heart

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A motion planning method based on HRL for autonomous exoskeleton

A motion planning method based on HRL for autonomous exoskel...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Dong, Yao He, Yong Su, Zhilong Ni, Jiangpeng Feng, Wei Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

With the increase of the world's population and the serious aging phenomenon, more and more people with mobility impairments need technology to assist them in their daily lives. Autonomous Exoskeleton are one of the important technologies. The existing gait generation algorithm are most based on simplified mathematical model calculations, which have the problem of poor accuracy and unfriendliness to the wearer. This research aims to propose a path planning and gait generation policies (HPPO) based on hierarchical reinforcement learning (HRL), so that the wearer can safely avoid obstacles and maintain dynamic balance when using the autonomous exoskeleton controlled by this policy to walk. The high-level controller (HL) is responsible for path planning, it can quickly plan a reasonable exoskeleton footholds in the indoor environment. The low-level controller (LL) can imitate a given reference trajectory, learn a walking policy suitable for the exoskeleton, and it can follow the planned footholds, so as to achieve the purpose of avoiding obstacles for the wearer. The algorithm is verified in simulation environment. © 2021 IEEE.

关键词： Motion planning

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Tbd-Y: Automatic Tea Bud Detection with Synergistic Object-Spatial Attention and Global-Local Attention Guided Feature Fusion

SSRN

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SSRN 2025年

作者： Liu, Zhongyuan Zhuo, Li Dong, Chunwang Li, Jiafeng Li, Yang Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Tea Research Institute Shandong Academy of Agricultural Sciences Jinan250100 China Key Laboratory of Tea Quality and Safety Control Ministry of Agriculture and Rural Affairs Tea Research Institute Chinese Academy of Agricultural Sciences Hangzhou China Shandong Academy of Agricultural Sciences China

Automatic Tea Bud Detection (TBD) is a critical technology in intelligent tea-picking systems. Nevertheless, challenges, such as complex environments and the high visual similarity between tea buds and backgrounds, frequently result in false detection and missed detection, especially for small tea buds. To address these issues, in this paper, an automatic TBD method is proposed, which is built upon the YOLOv11 object detection framework, named TBD-Y. Firstly, a Synergistic Object-Spatial Attention (SOSA) mechanism is proposed, which incorporates the proposed Local Context Attention (LCA) mechanism o enhance the features in both spatial and regional dimensions. It enables the network to focus more on the tea bud regions, and suppress the interference from background noise. Secondly, a Global-local Attention Guided Feature Fusion (GAGFF) strategy is designed. It consists of two branches: one branch enhances low-resolution, high-level features containing rich global semantic information, while the other branch strengthens high-resolution features that preserve low-level visual details. The fusion of these two branches improves the representation capability of the features. The SOSA and GAGFF are integrated into the YOLOv11 framework, constructing three variants of the TBD model with different parameter scales, named TBD-Y-L, TBD-Y-M, and TBD-Y-S. Experimental results on the self-built TBD dataset and the publicly available Global Wheat Head Dataset 2021 (GWHD_2021) demonstrate that the proposed TBD-Y-L outperforms the existing methods, achieving a superior detection accuracy. Furthermore, the TBD-Y-S model exhibits improved detection accuracy compared to YOLOv11-L, while maintaining lower model parameters and computational complexity. © 2025, The Authors. All rights reserved.

关键词： Object detection

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On-Chip Vectorial Structured Light Manipulation via Inverse Design

arXiv

引用

arXiv 2024年

作者： Lin, Xiaobin Wei, Maoliang Lei, Kunhao Wang, Zijia Wang, Chi Ma, Hui Ye, Yuting Zhan, Qiwei Li, Da Dai, Shixun Zhang, Baile Hu, Xiaoyong Li, Lan Li, Erping Lin, Hongtao State Key Laboratory of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou310024 China Laboratory of Infrared Materials and Devices Ningbo University Ningbo315211 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore637371 Singapore State Key Laboratory for Mesoscopic Physics Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Beijing100871 China MOE Frontier Science Center for Brain Science & Brain-Machine Integration Zhejiang University Hangzhou310027 China

On-chip structured light, with potentially infinite complexity1-31-3, has emerged as a linchpin in the realm of integrated photonics. However, the realization of arbitrarily tailoring a multitude of light field dimensions in complex media remains a challenge1, 4-6. Through associating physical light fields and mathematical function spaces by introducing a mapping operator, we proposed a data-driven inverse design method to precisely manipulate between any two structured light fields in the on-chip high-dimensional Hilbert space. To illustrate, light field conversion in on-chip topological photonics was achieved. High-performance topological coupling devices with minimal insertion loss and customizable topological routing devices were designed and realized. Our method provides a new paradigm to enable precise manipulation over the on-chip vectorial structured light and paves the way for the realization of complex photonic functions. © 2024, CC BY.

关键词： Topology

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Short-time Fourier transform covariance and selection, a feature extraction method for binary motor imagery classification

Short-time Fourier transform covariance and selection, a fea...

引用

2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Ma, Yue Zheng, Liangsheng Yi, Zhengkun Xiao, Yang Wang, Can Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chongqing University of Posts and Telecommunications

ISBN: (纸本)9781665436786

The brain-computer interfaces (BCI) technology is able to help dysfunctional people recover their motor functions. Electroencephalography (EEG) is an effective noninvasive method to construct BCI. Motor imagery (MI) paradigm can directly reflect the motor intention of user without additional stimulation equipment in EEG-BCI. The feature extraction methods are the key components to improve the accuracy of MI classification. Traditional feature extraction methods like CSP that only extract features in a single domain or two domains. In this study, we propose two novel feature selection method, short-time Fourier transform covariance and its selection method, which are aiming to extract spatial-time-frequency features simultaneously. In order to evaluate the proposed features, the BCI Competition IV Data Set IIb is employed to test the classification accuracy. By comparing the average accuracy of novel TSGSP method, the proposed method is more stable than TSGSP about 6% and the accuracy is just decrease about 0.5% at the same time. The average accuracy of 83.8% over all subjects is obtained. Superior classification performance results show that our proposed method has great potential, which is helpful for the further development and application of BCI technology for motor imaging in the field of neurorehabilitation. © 2021 IEEE.

关键词： Electroencephalography

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