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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Huang, Shizhong Miao, Jun Yao, Hui Zheng, Yilin Chu, Jun Zhang, Guoxiang Nanchang Hangkong University School of Aeronautical Manufacturing Engineering Nanchang330063 China Nanchang Hangkong University Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang330063 China University of California MercedCA95343 United States

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Layout estimation is one of fundamental tasks for understanding indoor scenes. In a single image of indoor scenes, key information, such as key points and boundaries for inferring the layout is often severely occluded or completely invisible. In this paper, a novel end-to-end deep learning network, which is extended from Xception module, is constructed for segmentation of semantic planes to infer the room layout structure. Moreover, the network contains an optimized depth wise separable convolution (DSC) in Xception module and a reinforced spatial attention module (SAM), so that it obtained good learning ability in severely occluded areas. Tests on public datasets show that the proposed network has a lower pixel error (PE) and higher generalization compared with existing algorithms. Ablation experiments prove that optimized DSC and SAM in the network are effective in improving the accuracy of layout estimation. © 2022 IEEE.

关键词： Semantic Segmentation

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An Improved Embedding Model for Zero-Shot Classification based on Attention Mechanism

An Improved Embedding Model for Zero-Shot Classification bas...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Zongfeng Wu Qishen Li Xiao Yu Xu Peng School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

It is well-known that the auxiliary information plays a key role in zero-shot classification. However, most of the existing popular methods do not make effective use of auxiliary information. To address this issue, we propose an improved embedding model for zero-shot classification based on attention mechanism, called EMAM. In the proposed EMAM, we first add an attention mechanism to effectively extract the key information of auxiliary information in zero-shot classification. Then optimizes the objective function to improve the recognition rate of this model. Finally the experimental comparison is implemented on the standard zero-shot learning datasets. The experimental results demonstrate that our proposed EMAM not only verifies its validity, but also achieves good results.

关键词： Automation Conferences Linear programming Data mining Information technology Standards

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Human Pose Estimation Based on Multi-resolution Feature Fusion Network

Human Pose Estimation Based on Multi-resolution Feature Fusi...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Weibai Duan Qishen Li Sihao Yuan Xiao Yu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

This article focuses on the study of multi-resolution feature fusion methods for human pose estimation. Most of the existing pose estimation methods are based on high-resolution gradually reducing the resolution to learn advanced semantic features, and then gradually recovering high-resolution features from the low-resolution semantic features to locate keypoints of the person instance. The process without fusion of rich features will inevitably lose more spatial and semantic information. The purpose of the multi-resolution feature fusion method proposed in this paper is to fuse spatial and semantic information on multiple scale feature maps, in addition, our network use the multi-receptive field fusion module on the same scale features to enhance feature extraction. Our methods can utilize more feature information to accurately locate keypoints.

关键词： Fuses Semantics Pose estimation Feature extraction Spatial resolution Kernel Information technology

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Scene Text Detection with Feature Aggregation and Receptive Field Enhancement

Scene Text Detection with Feature Aggregation and Receptive ...

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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Qin Tang Qishen Li Ruirui Wang Yanming Lai School of Information Engineering Nanchang Hangkong University Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

We propose a scene text detection with feature aggregation and receptive field enhancement. It mainly improves the problem of wrong text segmentation. Specifically, a Feature Aggregation and Receptive Field Enhancement Module (FARE) adopts a ladder structure and constructs multi-scale feature aggregation with multiple branches to enhance the feature representation ability of texts of different scales. We add dilated convolution to expand the range of receptive field, which increases the detection of large-scale texts in low-level feature and obtains more accurate position information. Extensive experiments on the ICDAR 2015, ICDAR 2017 MLT, and CTW1500 show that the proposed method effectively improves the detection performance of the network. Notably, our proposed method has detected a precision of 87.4% on the curved text dataset CTW1500.

关键词： Automation Convolution Fuses Conferences Benchmark testing Feature extraction Information technology

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A Control Method for Electric Cylinder Based on Three-Order ADRC

A Control Method for Electric Cylinder Based on Three-Order ...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Zhifan Jiang Zhihua Chen Lijun Tong Jiale Huang Yufei Che Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

As an executive component of robots, the precise control of electric cylinders is crucial for achieving accurate control of robots. This article proposes a position closed-loop electric cylinder control method based on three-orders active disturbance rejection control (ADRC). Firstly, model the electric cylinder system and determine its transfer function. Secondly, PID controllers and ADRC controllers were designed separately, and their stability was verified through the Routh criterion and Lyapunov method, respectively. Finally, the control effects of PID and ADRC were compared through three sets of experiments. The results show that compared to PID, ADRC is less affected by the increase in input signal frequency, has stronger anti-interference ability, and can effectively resist the influence of internal uncertainty.

关键词： Robust control Automatic frequency control PI control Uncertainty Stability criteria Transfer functions Resists PD control Robots Lyapunov methods

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Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain

Motion Planning and Tracking Control Method Based on Coppeli...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jiale Huang Zhihua Chen Yongbo Zhong Wenrui Zeng Chuanmin Ji key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China

Path planning and tracking algorithms are one of the cores of autonomous navigation technology for unmanned ground vehicle (UGV). In this paper, a local obstacle avoidance method based on event-triggered control is proposed to ensure that the UGV avoids obstacles in the tracking process. First, the path planning and tracking control problem of the UGV is modeled and formulated. Second, the improvement and logical correlation of the relevant algorithms are described in detail. Finally, ajoint simulation of MATLAB and V-rep is performed. The simulation results show that event-triggered control can largely improve the reliability of the UGV path planning and tracking process, and verify the effectiveness of the algorithm.

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Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Prototype-guided Unsupervised Domain Adaptation for Semantic...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Shaoqi Xia Hua Huang Qishen Li Yifan He Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Software Nanchang Hangkong University Nanchang China

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52.

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Fre-Yolo: Feature Refinement Extraction Network with Yolo for Blade Tip Small Point Light Detection

SSRN

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SSRN 2024年

作者： Zheng, Wenhao Xiong, Bangshu Yi, Hui Au, Qiaofeng Chen, Jiujiu Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang330063 China China Helicopter Research and Development Institute China

Detecting blade tip point light sources based on airborne computer vision is a critical step in measuring blade tip distance for coaxial unmanned helicopters. However, detecting blade tip point light sources quickly and highly accurately in a helicopter airborne environment is a challenge due to the extremely tiny imaging size of the blade tip point light sources. To this end, we propose a FRE-YOLO for detecting blade tip point light sources based on the YOLO network structure. In this work, we focus on designing a Feature Refinement Extraction network (FRE), which can greatly enrich the feature information of small targets during Backbone downsampling. Secondly, we also design a new paradigm Smooth Gaussian Distance (SmoothGD) which measures the degree of target bounding box matching. SmoothGD is mainly applied to regression loss calculation and label assigner, which can effectively improve the small target detection accuracy. Meanwhile, we construct the Blade Tip Point Light (BTPL) dataset for the network training. In addition, in order to evaluate the effectiveness of the proposed method more fairly, this paper conducts the same experiments on BTPL and the small target dataset VisDrone on MMYOLO. A large amount of experimental data shows that the proposed method has a positive impact on the detection of tiny point sources at the tip of the blade. It is also at the advanced level in the field of generalized small object detection. © 2024, The Authors. All rights reserved.

关键词： Extraction

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A New Method for recognition Partially Occluded Curved Objects under Affine Transformation

A New Method for Recognition Partially Occluded Curved Objec...

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International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： GuiMei Zhang JiYuan Xu JianXin Liu Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Province China School of Mechanical Engineering and Automation Xihua University Chengdu Sichuan China

A new method dealing with recognition of partially occluded and affine distortion objects is presented. The method is designed for objects with smooth curved boundary. It divides an object into affine-invariant parts based on the feature point. And a new approach for matching each part is presented in this paper. Robust Hausdorff distance (RHD) is introduced to measure the similarity between feature points set of model and that of target. In terms of the new RHD, the optimal affine transform can be estimated. And then the sub-curve match pairs are calculated based on the optimal affine transformation. The experimental results show proposed algorithm are capable of coping with partial occlusion and affine transformation.

关键词： Transforms Shape Robustness pattern recognition image recognition Mathematical model Distortion

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Research on Multi-level Attention-based Human Pose Estimation

Research on Multi-level Attention-based Human Pose Estimatio...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Jun Gao Hua Huang QiShen Li ShaoQi Xia School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network.

关键词： Computational modeling High performance computing Pose estimation Lighting Feature extraction Cognition Sun

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