2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第2页

Design of an autonomous robot system for oil sampling in ultra-high voltage substation 超高压变电站自动采油机器人系统的设计

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872250

Yingke Mao, Jianmin Wu, Zhengyi Zhu, Yong Zhou, Jia Chen, Min Zhao, Yiming Huang, Jianjun Yuan

引用次数: 0

A Data Annotation and Recognition Method Based on Zero Statistical Hypothesis Test and Multi Variable Binary Classification Theory 基于零统计假设检验和多变量二分类理论的数据标注与识别方法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872218

Zhan Ying, Chen Chao, Wang Lei, Shuai Zhao, Xianglei Zhu

{"title":"A Data Annotation and Recognition Method Based on Zero Statistical Hypothesis Test and Multi Variable Binary Classification Theory","authors":"Zhan Ying, Chen Chao, Wang Lei, Shuai Zhao, Xianglei Zhu","doi":"10.1109/RCAR54675.2022.9872218","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872218","url":null,"abstract":"Based on typical Chinese natural driving data, from natural driving scenario data collection to scenario automatic labeling and classification, this paper proposed a specific scenario automatic labeling and classification method by using statistical tools and machine learning methods. The front vehicle cut-in data of more than 4000 typical road scenarios in China are collected and extracted, and the parametric statistics and analysis are carried out for the relevant 6 variables. Considering the statistical uncertainty of the variables, the statistical exclusion curve of “normal front vehicle cut-in scenario” is calculated by using the hypothesis test method based on the principle of mathematical statistics, by comparing the distribution curve of any event with the statistical exclusion curve, the annotation of the front vehicle entry scenario data is realized. At the same time, using the positive and negative sample classification method of machine learning based on bagging decision tree classifier, the integrated learning classification method based on boosting decision tree, and the depth learning method based on improved resnet-18 convolution Network + LSTM recurrent neural network, the multi-variable binary classifiers are trained respectively to realize the classification task of the front vehicle cut-in scenario. Furthermore, comparing the three classification methods, the test results on the verification show that the BDT classifier has the best result, effectively realizes the classification tasks of “dangerous front vehicle cut-in scenario” and “normal front vehicle cut-in scenario”, and this technical tool chain can be reused in the fine-grained classification of other driving scenes in the future","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124144305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robust Object Tracking and Visual Servo Method for Mobile Robot 移动机器人鲁棒目标跟踪与视觉伺服方法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872244

Lu-yi Chen, Mingdi Niu, Sheng Wang, Peng Wu, Yuanhao Li

{"title":"A Robust Object Tracking and Visual Servo Method for Mobile Robot","authors":"Lu-yi Chen, Mingdi Niu, Sheng Wang, Peng Wu, Yuanhao Li","doi":"10.1109/RCAR54675.2022.9872244","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872244","url":null,"abstract":"The general Siamese network based object tracking methods tend to generate the final score map from high-level features and treat features from each position equally, which may lead to the problems of large search region and low efficiency. In order to solve these, this paper proposes a fully-connected Siamese network tracking method based on the calculation of histogram of gradient feature similarity and on feedback of the fading-memory Kalman filter. This strategy enables real-time correction and compensation, which means it could re-track the target although it is occluded or temporarily lost. The target’s bounding box obtained by object tracking method is used to produce the control command and achieve the image-based visual servo. Comparative experiments with other methods are conducted on several public datasets to prove its effectiveness. In addition, we design a mobile robot tracking system to test the algorithmic performance in real-world scenarios. Experimental results show that the robot is able to track the target accurately, and continue to track the target despite occlusion or temporary disappearance.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124188682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Real-time Ship Detection System for Large-Scale Optical Remote Sensing Image on Micro-Nano Satellite 微纳卫星大尺度光学遥感图像实时舰船检测系统

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872279

Weihong Chen, Bowen Yao, Yutong Li, Liansheng Liu, Jun Liang

{"title":"A Real-time Ship Detection System for Large-Scale Optical Remote Sensing Image on Micro-Nano Satellite","authors":"Weihong Chen, Bowen Yao, Yutong Li, Liansheng Liu, Jun Liang","doi":"10.1109/RCAR54675.2022.9872279","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872279","url":null,"abstract":"Ship detection in optical remote sensing images is of great importance for maritime traffic management. At present, the advanced optical system on the micro-nano satellites has been able to generate large-scale remote sensing images of gigabits data in real-time. However, the image processing system cannot manage such a huge amount of data and finish the ship detection task within the time constraint. To address this issue, this article contributes a large-scale remote sensing image processing system for real-time ship detection on micro-nano satellite. By introducing the heterogeneous System-On-Chip (SoC) and Field Programmable Gate Array (FPGA) processors to the hardware design with distributed memory access architecture, the high throughput requirements of large-scale image acquisition and processing strategies including sliding window crop, grayscale variance calculation and convolutional neural networks are successfully satisfied. The implementation and evaluation of the proposed system demonstrate its effectiveness in real-time ship detection in large-scale remote sensing images. With the large-scale remote sensing image as the input, the designed system achieves up to 3. 2Gbps of image data throughput for ship detection in real-time.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123606105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Sandwich Model Based Internal Model Control for EGR Valve 基于夹心模型的EGR阀内模控制

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872154

Xinyi Tang, Ruili Dong, Yonghong Tan

引用次数: 1

A Smoke and Flame Detection Method Using an Improved YOLOv5 Algorithm 使用改进型 YOLOv5 算法的烟雾和火焰探测方法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872297

Tong Yang, Sheng Xu, Weimin Li, Haibin Wang, Guodong Shen, Qiang Wang

{"title":"A Smoke and Flame Detection Method Using an Improved YOLOv5 Algorithm","authors":"Tong Yang, Sheng Xu, Weimin Li, Haibin Wang, Guodong Shen, Qiang Wang","doi":"10.1109/RCAR54675.2022.9872297","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872297","url":null,"abstract":"The complex background scenes in traditional fireworks detection methods make flame identification challenging and complicated. This paper focuses on improving the detection efficiency and accuracy of flame disasters. First, the data augmentation strategy and label smoothing are used to preprocess the sample set, which solves the over-fitting problem caused by the insufficient number of samples. Second, we add Convolutional Block Attention Module (CBAM) before each backbone classifier, to compress and re-weight the input features from two independent channel and space dimensions. By focusing on smoke and fire’s feature information, the ability of desired feature extraction is strengthened. Third, the Focal loss function is utilized to enhance the weights of complex samples. Consequently, the imbalance problem about positive and negative samples in single-stage detection, and the high proportion of easy-to-separate samples in the loss function are both resolved. Experimental examples demonstrate that the proposed network is easy to converge and expand, which guarantees detection accuracy and satisfies detection speed requirements.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129394240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Dynamics Modeling with Realistic Constraints for Trajectory Tracking Control of Manipulator* 机械臂轨迹跟踪控制的现实约束动力学建模*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872303

Lu Liu, Guoyu Zuo, Jiangeng Li, Jianfeng Li

引用次数: 0

Upper and lower limb linkage design and training trajectory planning of rehabilitation robot 康复机器人上下肢机构设计与训练轨迹规划

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872195

Jianjun Yuan, Zhidong Zheng, Sheng Bao, Liang Du, Tong Zhou

引用次数: 1

Research on Manipulator Attitude Planning and Transition Algorithm Based on Cardinal Spline 基于基数样条的机械臂姿态规划与过渡算法研究

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872284

Huo Feipeng, Ge Rui, Sun Yingkai, Liu Xu, Lei Junsong, Wang Hong

引用次数: 0

Unattended Substation Inspection Algorithm Based on Improved YOLOv5 基于改进YOLOv5的变电站无人值守巡检算法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872227

Guangxin Dai, Yue Yuan, Weijie Huang, Qiang Liu, Chang-Hwan Ju, Xiaona Liu, Menghua Zhang

引用次数: 3