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检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

共 18150 条记录，以下是921-930 订阅

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A Generative Adversarial Network Based Approach for Accurate Detection of Bright Spots in Photovoltaic Panels 11

A Generative Adversarial Network Based Approach for Accurate...

引用

11th IEEE international conference on signal processing and Integrated Networks, SPIN 2024

作者： Thirwani, Anand Nair, Rahul Kulkarni, Kedar Reliance Industries Ltd. Delhi India Reliance Industries Ltd. Kochi India Reliance Industries Ltd. Bangalore India

ISBN: (纸本)9798350308433

Ensuring the safe and efficient operation of photovoltaic (PV) solar panels requires early detection of defects during the manufacturing process itself. Among these defects, the presence of 'bright spots' on the surface of PV solar panels poses a major concern as they can lead to excessive energy production which further leads to short circuits, increased heating, and potentially even fires. In this paper, we propose a robust approach to reliably detect bright spots on the surface of PV cells that are based on image classification techniques enhanced by novel feature extraction and generative adversarial networks (GANs). The proposed approach demonstrated robust performance on Electro-Luminescence (EL) images from a real, operating PV solar panel manufacturing unit with a recall of 0.93 and an F1-score of 0.93, offering a reliable solution to detect bright spots in PV cells. © 2024 IEEE.

关键词： Computer vision

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vision-Guided robot for Agriculture Assistance 6th

Vision-Guided Robot for Agriculture Assistance

引用

6th international and 21st National conference on Machines and Mechanisms, iNaCoMM 2023

作者： Patil, Rohit R. Nayak, Sandesh Kabojinavar, Hemanthkumar J. Sankangoudar, Shivaprasad S. Channammanavar, Vishal B. Moodbidri Karnataka Mangalore574225 India

ISBN: (纸本)9789819754229

In every corner of the world, people depend on the agriculture as one of the major basic needs in their daily life. Every farmer aims at growing better quality agricultural products. However, the pests do harm the crops and thereby cause a decrease in the quality of crops. Usually pesticides are used to stop pests, but extensive usage of pesticides is harmful for the nature. Agricultural research aims in providing solutions to increase the quality of crops. Addressing the issue, an effort is made to develop a solution by early identifying the pest and providing recommended solution to the farmer. Here, image-processing technique is used to detect the location of pests during the early stage. A mini robot attached with an image-capturing camera as its end effector is designed and developed. The robot takes the camera to different parts of the crops and captures the images. The captured images are compared with the previously trained software programme. An algorithm is developed for detection and classification of pest where captured image is compared with the dataset and a preventive measure towards pests is suggested. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microrobots

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Automatic Radio Map Updating with Multi-Scale Feature for Wireless Indoor Localization 15

Automatic Radio Map Updating with Multi-Scale Feature for Wi...

引用

15th IEEE international conference on Wireless Communications and signal processing, WCSP 2023

作者： Shi, Lin Jiang, Weina Wu, Di Niu, Qun Liu, Ning Sun Yat-Sen University School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350324662

The accuracy of fingerprint-based indoor localization strongly relates to the precision of the wireless radio map. However, radio maps are vulnerable to deployment changes and require constant maintenance, which is labor-intensive and time-consuming. To address this issue, we propose an automated radio map update method that employs a mobile robot to collect fingerprints along its path. Considering the relationship between signal strength and propagation distance, we design a reconstruction network combining multi-scale features in radio map updating, which estimates signal strengths based on local and global distributions. In addition, we propose a dynamic strategy with an attention mechanism to select a set of crucial representative points (RPs) for a sparse survey in the update stage. The chosen points have stronger relations to the surroundings than other points, improving our method's reconstruction accuracy. The experimental results show that the proposed method reconstructs the radio maps of our two test sites which outperforms the state-of-art approaches in reconstruction accuracy by 32.03%. And the localization accuracy of the updated radio maps is significantly improved by about 51.39%. © 2023 IEEE.

关键词： Deep learning Indoor Localization Mobile robot Radio map updating

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Classification of Whale Call signals Based on the Improved YOLOv5 Model 6

Classification of Whale Call Signals Based on the Improved Y...

引用

6th international conference on Information Communication and signal processing, ICICSP 2023

作者： Zhang, Ruolan Shi, Yang Yang, Long Zhou, Zihao School of Marine Science and Technology Northwestern Polytechnical University Shaanxi Key Laboratory of Underwater Information Technology Xi'an China

ISBN: (纸本)9798350339994

Underwater target classification is an important research topic in the field of underwater acoustic signal processing, and the classification of marine mammal vocalizations is of great significance for the conservation of them. In this paper, the calls of humpback whales, short-finned pilot whales and sperm whales are taken as the research objects and plotted as spectrograms. Aiming to address the issue of small targets in spectrograms, we proposed an improved model based on YOLOv5 and integrate the vision Transformer BiFormer module to enhance the detection accuracy of each sample. The test result shows that the mean average precision of all categories in the improved model is 88.1%, a 4.9% increase compared to the pre-improvement performance. © 2023 IEEE.

关键词： underwater target classification vision transformer whale calls YOLOv5

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An Adaptive Two-Step Method for Voluntary Muscle Activity Detection Using sEMG signals with False Background Spikes 10

An Adaptive Two-Step Method for Voluntary Muscle Activity De...

引用

10th international conference on Electrical Engineering, Control and robotics (EECR)

作者： Zheng Nan Li Yurong Zhan Miaoqin Fuzhou Univ Sch Elect Engn & Automat Fuzhou Peoples R China

ISBN: (数字)9798350370003

ISBN: (纸本)9798350370010;9798350370003

Accurate detection of muscle activity is crucial for rehabilitation systems that rely on voluntary control. However, the presence of false background spikes interference can significantly impede the precise decoding of motion intention. To address this problem, an adaptive two-step method is proposed in this paper for accurately extracting the muscle activation intervals from sEMG signals. In the first step, an adaptive threshold is used to identify potential onsets and offsets of the envelope. Then, a combination of an evaluation equation and the k-means clustering technique is utilized to eliminate incorrect onsets and offsets. In the second step, two peak points closing proximity to the onset and offset are identified. The tangency of the envelope's onset and offset with the respective peak points is then determined, with the intersection point of these tangencies is considered as the final onset and offset. The proposed method is tested on semi-synthetic sEMG signals and real sEMG signals, and compared with state-of-the-art algorithms. The results clearly indicate that the proposed method produces the best detection performance, and eliminates the requirement for parameter selection, greatly facilitating the signal extraction process.

关键词： surface electromyography signal muscle activity detection signal processing adaptive two-step method

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Learnt Mutual Feature Compression for Machine vision 48

Learnt Mutual Feature Compression for Machine Vision

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Liu, Tie Xu, Mai Li, Shengxi Chen, Chaoran Yang, Li Lv, Zhuoyi Beihang University Department of Electronic and Information Engineering Beijing China Vivo Mobile Communication Company Limited Beijing China

ISBN: (纸本)9781728163277

Recently, image coding for machines (ICM) has been playing an important role in facilitating intelligent vision tasks. Unfortunately, the existing ICM methods separately compress features at each scale, neglecting the redundancy across multi-scale features. To address this issue, this paper proposes an end-to-end mutual compression framework for the ICM, such that the compression efficiency can be significantly improved by removing the cross-scale redundancy. Specifically, the proposed framework consists of a mutual feature compression network (MFCNet) and a basic feature compression network (BFCNet). The MFCNet predicts large-scale features from basic small-scale features, such that the large amount of bitrates assigned to compress large-scale features can be saved. Moreover, the BFCNet is proposed to compress small-scale features of high quality by removing spatial and channel-wise redundancy. This guarantees superior performances whilst consuming extremely small amount of bit-rates. The experimental results show that our method achieves 90.10% and 74.97% BD-rate saving against the VVC feature anchor and VVC image anchor that have been recently accepted by the moving picture experts group (MPEG). © 2023 IEEE.

关键词： Learnt feature compression machine vision mutual redundancy

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Optimizing vision Transformers for Medical Image Segmentation 48

Optimizing Vision Transformers for Medical Image Segmentatio...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Liu, Qianying Kaul, Chaitanya Wang, Jun Anagnostopoulos, Christos Murray-Smith, Roderick Deligianni, Fani University of Glasgow School of Computing Science United Kingdom University of Warwick United Kingdom

ISBN: (纸本)9781728163277

For medical image semantic segmentation (MISS), vision Transformers have emerged as strong alternatives to convolutional neural networks thanks to their inherent ability to capture long-range correlations. However, existing research uses off-the-shelf vision Transformer blocks based on linear projections and feature processing which lack spatial and local context to refine organ boundaries. Furthermore, Transformers do not generalize well on small medical imaging datasets and rely on large-scale pre-training due to limited inductive biases. To address these problems, we demonstrate the design of a compact and accurate Transformer network for MISS, CS-Unet, which introduces convolutions in a multi-stage design for hierarchically enhancing spatial and local modeling ability of Transformers. This is mainly achieved by our well-designed Convolutional Swin Transformer (CST) block which merges convolutions with Multi-Head Self-Attention and Feed-Forward Networks for providing inherent localized spatial context and inductive biases. Experiments demonstrate CS-Unet without pre-training out- performs other counterparts by large margins on multi-organ and cardiac datasets with fewer parameters and achieves state-of-the-art performance. Our code is available at Github1. © 2023 IEEE.

关键词： Convolution

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Implementation and Design of Agile and Multipurpose Autonomous robot Using ROS

Implementation and Design of Agile and Multipurpose Autonomo...

引用

international conference on VLSI, signal processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE-2022

作者： Jeevan, K. Rai, Sohan M. Kumar, V. Mukesh Manish, E.S. Sathish, Pranav Singh, Chandra Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Mangaluru India

ISBN: (纸本)9789819944439

This paper discusses an implementation of autonomous navigation functionality on a differential drive mobile platform called AMAR (Agile and Multipurpose Autonomous robot) based on the robot Operating System (ROS). The paper's primary contribution is to describe an unmanned platform capable of mapping, localizing, and navigating in an enclosed environment that is well-suited for industrial operations. The approach used for mapping is through the ROS framework, a technology stack containing multiple software stacks for controlling, visualization, and debugging the robot, wrapped with the help of sensors such as RGBD camera and LiDAR (Light Detection and Ranging). For navigation, Adaptive Monte Carlo Localization is used for localizing the vehicle and thus leading to autonomous operation. The proposed solution is computationally efficient and capable of dynamic obstacle avoidance. The simulations were performed using the 3D physics simulator Gazebo and the results were visualized using RViz. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： robot Operating System

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vision Transformer with Progressive Tokenization for CT Metal Artifact Reduction 48

Vision Transformer with Progressive Tokenization for CT Meta...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Zheng, Songwei Zhang, Dong Yu, Chunyan Zhu, Danhong Zhu, Longlong Liu, Hao Huang, Zhongzheng Fuzhou University College of Computer and Data Science China Fuzhou University Zhicheng College China

ISBN: (纸本)9781728163277

High-quality Computed Tomography(CT) plays a vital role in clinical diagnosis, but the presence of metallic implants will introduce severe metal artifacts on CT images and obstruct doctors' decision-making. Many prior researches on Metal Artifact Reduction(MAR) are based on Convolutional Neural Network(CNN). Recently, Transformer has demonstrated phenomenal potential in computer vision. Also, transformer-based methods have been harnessed in CT image denoising. Nevertheless, these methods have been little explored in MAR. To fill the gap, we put forth, to the best of our knowledge, the first transformer-based architecture for MAR. Our method relies on a standard vision Transformer(ViT). Furthermore, we tap into the progressive tokenization to refrain from the simple tokenization of ViT which gives rise to inability to model the local anatomical information. Additionally, for the sake of facilitating the interaction among tokens, we take advantage of cyclic shift from Swin Transformer. Finally, many experiment results reveal that the transformer-based technique is superior to those on the basis of CNN to some degree. © 2023 IEEE.

关键词： CT Deep Learning Metal Artifact Reduction Transformer

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Vitasd: Robust vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis 48

Vitasd: Robust Vision Transformer Baselines for Autism Spect...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Cao, Xu Ye, Wenqian Sizikova, Elena Bai, Xue Coffee, Megan Zeng, Hongwu Cao, Jianguo New York University New York United States Shenzhen Children's Hospital Shenzhen China Nyu Grossman School of Medicine New York United States

ISBN: (纸本)9781728163277

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with very high prevalence around the world. Research progress in the field of ASD facial analysis in pediatric patients has been hindered due to a lack of well-established baselines. In this paper, we propose the use of the vision Transformer (ViT) for the computational analysis of pediatric ASD. The presented model, known as ViTASD, distills knowledge from large facial expression datasets and offers model structure transferability. Specifically, ViTASD employs a vanilla ViT to extract features from patients' face images and adopts a lightweight decoder with a Gaussian Process layer to enhance the robustness for ASD analysis. Extensive experiments conducted on standard ASD facial analysis benchmarks show that our method outperforms all of the representative approaches in ASD facial analysis, while the ViTASD-L achieves a new state-of-the-art. Our code and pretrained models are available at https://***/IrohXu/ViTASD. © 2023 IEEE.

关键词： Autism Spectrum Disorder Knowledge Distillation (KD) Transfer Learning vision Transformer (ViT)

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