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Research on Selective filtering and PCNN for Nickel Foam Surface Defect Segmentation

IFAC-PapersOnLine 2018年第21期51卷 135-140页

作者： Li, Jianqi Zhu, Jiang Nie, Fangyan Cao, Binfang Huang, Lijuan Hunan Province Cooperative Innovation Center for The Construction & Development of Dongting Lake Ecological Economic Zone Hunan University of Arts and Science ChangdeHunan China College of Information Engineering Xiangtan University Xiangtan411105 China National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Changsha410082 China

As the key basis materials of Ni-MH and Ni/Cd battery electrode, the quality of the nickel foam products is closely related with the performance and safety of rechargeable batteries. In order to meet the requirements of automatic defect inspection, this paper proposes an image segmentation method for the appearance defects of nickel foam. Nickel foam image has the characteristics of a low degree of differentiation between foreground and background, basic structure of mutual adhesion, surface morphology for complex random texture features. In order to extract defects from complex nickel foam surface image, a novel selective filtering method is proposed to preprocess the original image, thereby reducing the effects of the above factors, and then a modified PCNN method is used to segment the defect area. The results show that the proposed method can effectively segregate the defects from the background, which lays a foundation for the automatic detection of the appearance quality of nickel foam. © 2018

关键词： Image segmentation Computer vision Nickel metal hydride batteries Surface defects Appearance qualities Automatic defect inspection Automatic Detection Defect inspection modified PCNN Nickel foam Segmentation methods Selective filtering

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Real time backbone for semantic segmentation

arXiv

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arXiv 2019年

作者： Yang, Zhengeng Yu, Hongshan Fu, Qiang Sun, Wei Jia, Wenyan Sun, Mingui Mao, Zhi-Hong National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China University of Pittsburgh University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering Department of Neurosurgery

The rapid development of autonomous driving in recent years presents lots of challenges for scene understanding. As an essential step towards scene understanding, semantic segmentation thus received lots of attention in past few years. Although deep learning based state-of-the-arts have achieved great success in improving the segmentation accuracy, most of them suffer from an inefficiency problem and can hardly applied to practical applications. In this paper, we systematically analyze the computation cost of Convolutional Neural Network(CNN) and found that the inefficiency of CNN is mainly caused by its wide structure rather than the deep structure. In addition, the success of pruning based model compression methods proved that there are many redundant channels in CNN. Thus, we designed a very narrow while deep backbone network to improve the efficiency of semantic segmentation. By casting our network to FCN32 segmentation architecture, the basic structure of most segmentation methods, we achieved 60.6% mIoU on Cityscape val dataset with 54 frame per seconds(FPS) on 1024 × 2048 inputs, which already outperforms one of the earliest real time deep learning based segmentation methods: ENet. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Adaptive robust RBFNNs-based model estimator for a small quadrotor aircraft robot

Adaptive robust RBFNNs-based model estimator for a small qua...

引用

IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Hang Zhong Shushuai Li Yaonan Wang Hongjian Liu College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology

ISBN: (纸本)9781509061914

This paper presents an on-line estimator that incorporates adaptive MIMO radical basis function neural networks (RBFNNs) for model identification of quadrotor unmanned aerial vehicles (UAVs). The inputs and outputs of quadrotor aircrafts can be obtained from dynamic models or real attitude and position sensors. The adaptive learning rate is employed in the gradient descent method for the update of the weights of RBFNNs, and Lyapunov approach guarantees the stability of the global convergence of the modeling errors. The Welsch functions are also employed as the error functions to get rid of the influence from the noise due to disturbances like wind gusts. Simulation results using robotics Toolbox for Matlab verify the effectiveness and robustness of the proposed estimator compared with results of traditional RBFNNs. Experiment results from real aircraft platform show that RBFNNs combining adaptive learning rate and Welsch error functions can approximate the overall system with high accuracy and robustness to disturbances.

关键词： Atmospheric modeling Aircraft Mathematical model Adaptation models Robustness robots MIMO

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Partial labeled gastric tumor segmentation via patch⇂based reiterative learning

arXiv

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arXiv 2017年

作者： Nan, Yang Coppola, Gianmarc Liang, Qiaokang Zou, Kunglin Sun, Wei Zhang, Dan Wang, Yaonan Yu, Guanzhen College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Laboratory for Robot Vision Perception and Control Hunan University Changsha Hunan410082 China Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Hunan418000 China Faculty of Engineering and Applied Science University of Ontario Institute of Technology OshawaONL1H 7K4 Canada Department of Mechanical Engineering York University TorontoONM3J 1P3 Canada Department of Oncology Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai201203 China

Gastric cancer is the second leading cause of cancer‐related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent. This assignment has high clinical relevance and would generally require vast microscopic assessment by pathologists. Recent advances in deep learning have produced inspiring results on biomedical image segmentation, while its outcome is reliant on comprehensive annotation. This requires plenty of labor costs, for the ground truth must be annotated meticulously by pathologists. In this paper, a reiterative learning framework was presented to train our network on partial annotated biomedical images, and superior performance was achieved without any pre‐trained or further manual annotation. We eliminate the boundary error of patch‐based model through our overlapped region forecast algorithm. Through these advisable methods, a mean intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09% on the partial labeled dataset was achieved, which made us win the 2017 China Big Data & Artificial Intelligence Innovation and Entrepreneurship Competitions1. Copyright © 2017, The Authors. All rights reserved.

关键词： Diseases

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