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Research on Key Technologies of target location based on Intelligent Robot 3

Research on Key Technologies of target location based on Int...

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3rd International conference on Computer vision, image and Deep Learning and International conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Li, Zhong Guo, Caihui Guangzhou City Construction College Guangdong China Citi University Ulaanbaatar Mongolia

ISBN: (纸本)9781665459112

In recent years, the country has proposed the strategic development goal of 'Made in China 2025', and the intelligent manufacturing industry has gradually received national attention. Intelligent robots rely on the advantages of high efficiency and strong adaptability to the working environment to promote the development of intelligent manufacturing industry. machine vision technology makes robots more intelligent, and the robot system integrating vision has also developed rapidly. Compared with monocular vision, binocular vision can obtain target stereo information, and it has become a key research topic in related fields. Under this background, this paper designs an intelligent robot target recognition and positioning system based on binocular vision according to the actual industrial demand. This paper mainly studies the recognition and localization algorithm of circular object workpiece by intelligent robot based on binocular vision system, and designs and realizes the whole experimental system according to the research content. Based on the system experiment, this paper introduces the calibration of the binocular camera system and the robot vision system, the target image pretreatment algorithm, the recognition and positioning algorithm of the target workpiece and so on. Finally, the visual interface is designed to control the robot to complete the simulated grasping action and realize the whole system. The object image is acquired by binocular camera, the object identification and location are completed according to the image processing algorithm, and the robot is controlled to complete the simulation grasping experiment, which has certain reference significance in practical application. © 2022 IEEE.

关键词： Binocular vision

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A Parallel Sea-Land Segmentation Method Based on Improved Unet Model 3

A Parallel Sea-Land Segmentation Method Based on Improved Un...

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3rd International conference on Computer vision and Pattern Analysis, ICCPA 2023

作者： Zhang, Xuebo Jiang, Min Li, Jinli School of Space Information Space Engineering University Beijing China 96944 troops Chinese People's Liberation Army Beijing China

ISBN: (纸本)9781510667563

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved Unet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on high-resolution remote sensing images. © 2023 SPIE.

关键词： image segmentation

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Feature-Guided machine-Centric image Coding for Downstream Tasks

Feature-Guided Machine-Centric Image Coding for Downstream T...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Kwak, Sangwoon Yun, Joungil Choo, Hyon-Gon Kim, Munchurl Electronics and Telecommunications Research Institute Korea Republic of Korea Advanced Institute of Science and Technology Korea Republic of

ISBN: (纸本)9798350313154

Video coding, a process of compressing and decompressing digital video content, has traditionally been optimized for human visual systems by reducing its size while maintaining the human perceptual quality. However, with the remarkable progress of artificial intelligence (AI) technology, the need for machine-centric coding has rapidly been increasing in recent years. In response to these trends, international standardization organizations such as MPEG are actively working to develop and launch new standards on coding technologies for machines, called video coding for machines (VCM). In this paper, we present a novel feature-guided block-wise image blending method for image compression, which is suitable for machine applications such as object detection and segmentation. For this, we use a gradient map of the feature loss using the pretrained encoder part of a task-specific network as guide for input degradation, so that the degraded input images can be effectively compressed for machine-centric tasks. Our method is simple but effective because additional training is not required by utilizing the pretrained encoder parts of networks for targeted tasks. Experimental results show that BD-rate gains can be obtained by applying our proposed method with averages 11% and 8% for object detection and instance segmentation tasks, respectively, compared to the image anchor results of MPEG-VCM reference software v0.4. © 2023 IEEE.

关键词： Computer vision

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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Application of Group-Aware Hierarchical Transformation Based on Grouping in Hyperspectral image Classification 3

Application of Group-Aware Hierarchical Transformation Based...

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3rd International conference on image processing, Computer vision and machine Learning, ICICML 2024

作者： Li, Xuyang Fan, Xiangsuo Li, Qi Li, Peng Zhao, Xueqiang Guangxi University of Science and Technology School of Automation Liuzhou China Sun Yat-sen University China Water Resources Pearl River Planning Surveying & Designing Co. Ltd Guangzhou China

ISBN: (纸本)9798350355413

Currently, deep learning algorithms shine in the field of hyperspectral image (HSI) classification and processing of hyperspectral images. Still, due to the limitations of the input size sensing field of deep learning algorithms, it is generally necessary to cut the input of HSI into fixed-size image chunks first, and then carry out the classification operation. This approach is limited by the size characteristics of HSI. In recent years, the visual transformer (ViT), as a deep learning algorithm that can be applied to multi-tasks, has not surprisingly been introduced to the HSI classification task, which is capable of selecting appropriate patch scales for different input maps through a flexible input feature selection mechanism. Nevertheless, when ViT deals with HSI with many bands, the features extracted by its global self-attention mechanism may exhibit a tendency of over-dispersion, which affects the classification performance. To address the above challenges, this paper proposes a ViT-based packet-aware hierarchical converter (PSHC) architecture that aims to optimize the classification of HSI. The architecture strengthens the local relationships within all spectral channels by embedding a pixel group mapping (PGM) module, which applies a global self-attention mechanism to the algorithm for extracting global spatial-spectral features and achieves better characterization by fusing global and local features. In addition, PSHC adopts a hierarchical structural design, which firstly effectively extracts and fuses multiscale features without enlarging the number of model parameters, significantly improving the classification accuracy. Experimental validation on two benchmark HSI datasets shows that the proposed PSHC method outperforms previous deep learning algorithms in HSI classification tasks, especially in city-like map applications. © 2024 IEEE.

关键词： Hierarchical Architecture Hyperspectral image Classification packet-aware hierarchical converter pixel group mapping

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Joint Demosaicing and Denoising with Frequency Domain Features 32nd

Joint Demosaicing and Denoising with Frequency Domain Featur...

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32nd International conference on Artificial Neural Networks (ICANN)

作者： Li, Feiyu Yang, Jun Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing Peoples R China

ISBN: (纸本)9783031441974;9783031441981

image demosaicing and denoising play important roles in modern digital camera image processing and have been studied in recent decades. Due to the interference of independent demosaicing and denoising causes color errors and the loss of image details, therefore, the joint demosaicing and denoising problem has been extensively studied. Although the simple combination of existing demosaicing and denoising methods can obtain recovery images, there are still considerable room for improvement. In this paper, we propose an end-to-end frequency domain features network (FFNet) to solve the ill-posed joint problem. Different from previous methods based on spatial domain features, FFNet uses frequency domain information to learn global and local image features, and it is based on the vision Transformer architecture. Our model contains mainly two frequency domain feature blocks: a global Fourier block (GFB) that learns a global frequency domain parameter weight to achieve global attention and a local Fourier block (LFB) based on multi-layer perceptron to enhance local feature learning. These two modules and channel attention are combined into the frequency domain attention block (FAB), which constitutes the core module of FFNet. Extensive experiments demonstrate that FFNet outperforms previous approaches and achieves state-of-the-art performance.

关键词： image demosaicing and denoising joint problem frequency domain network

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A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation 3

A Combined Encoder and Transformer Approach for Coherent and...

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3rd International conference on image processing, Computer vision and machine Learning, ICICML 2024

作者： Chen, Jiajing Wang, Shuo Qi, Zhen Zhang, Zhenhong Wang, Chihang Zheng, Hongye New York University New York United States Purdue University Indianapolis Indianapolis United States Northeastern University Boston United States George Washington University George WA United States The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350355413

This research introduces a novel text generation model that combines BERT's semantic interpretation strengths with GPT-4's generative capabilities, establishing a high standard in generating coherent, contextually accurate language. Through the combined architecture, the model enhances semantic depth and maintains smooth, human-like text flow, overcoming limitations seen in prior models. Experimental benchmarks reveal that BERT-GPT-4 surpasses traditional models, including GPT-3, T5, BART, Transformer-XL, and CTRL, in key metrics like Perplexity and BLEU, showcasing its superior natural language generation performance. By fully utilizing contextual information, this hybrid model generates text that is not only logically coherent but also aligns closely with human language patterns, providing an advanced solution for text generation tasks. This research highlights the potential of integrating semantic understanding with advanced generative models, contributing new insights for NLP, and setting a foundation for broader applications of large-scale generative architectures in areas such as automated writing, question-answer systems, and adaptive conversational agents. © 2024 IEEE.

关键词： BERT BLEU Encoder GPT-4 Large Language Models Natural Language processing Perplexity Text Generation

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3D Object Reconstruction with Deep Learning 13th

3D Object Reconstruction with Deep Learning

引用

13th IFIP TC 12 International conference on Intelligent Information processing, IIP 2024

作者： Aremu, Stephen S. Taherkhani, Aboozar Liu, Chang Yang, Shengxiang School of Computer Science and Informatics De Montfort University Leicester United Kingdom Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783031579189

Recent advancements and breakthroughs in deep learning have accelerated the rapid development in the field of computer vision. Having recorded a huge success in 2D object perception and detection, a lot of progress has also been made in 3D object reconstruction. Since humans can infer and relate better with 3D world images by just a single view 2D image of the object, it is necessary to train computers to think in 3D to achieve some key applications of computer vision. The use of deep learning in 3D object reconstruction of single-view images is rapidly evolving and recording significant results. In this research, we explore the Facebook well-known hybrid approach called Mesh R-CNN that combines voxel generation and triangular mesh reconstruction to generate 3D mesh structure of an object from a 2D single-view image. Although the reconstruction of objects with varying geometry and topology was achieved by Mesh R-CNN, the mesh quality was affected due to topological errors like self-intersection, causing non-smooth and rough mesh generation. In this research, Mesh R-CNN with Laplacian Smoothing (Mesh R-CNN-LS) was proposed to use the Laplacian smoothing and regularization algorithm to refine the non-smooth and rough mesh. The proposed Mesh R-CNN-LS helps to constrain the triangular deformation and generate a better and smoother 3D mesh. The proposed Mesh R-CNN-LS was compared with the original Mesh R-CNN on the Pix3D dataset and it showed better performance in terms of the loss and average precision score. © IFIP International Federation for Information processing 2024.

关键词： Computer vision

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Depth and Thermal images in Face Detection - A Detailed Comparison Between image Modalities 2022

Depth and Thermal Images in Face Detection - A Detailed Comp...

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5th International conference on machine vision and applications (ICMVA)

作者： Mucha, Wiktor Kampel, Martin TU Wien Comp Vis Lab Vienna Austria

ISBN: (纸本)9781450395670

Face detection is a well-known issue in image processing, and numerous studies are present in this field. A prominent part of the work is devoted to RGB images, leaving depth and thermal data with less interest. However, in some conditions like low-light areas where face detection is needed, non-RGB sensors might perform better. Also, mounting an additional RGB camera could be challenging or not possible, considering privacy concerns. In this work, current deep learning methodologies are employed to train depth and thermal detection models. The training is done using combined publicly available data that is processed by us for this purpose in order to create necessary annotations for a learning process. The resulting models are validated on a new trimodal dataset collected for this experiments purpose. It contains images captured with RGB, depth, and thermal sensors. Various scenes with single and multiple faces appearances can be found. The results show that non-RGB solutions can be applied in practice with highly robust accuracy and their efficiency is close to RGB detectors. However, their performance depends on the environment and that circumstances are described later in this article.

关键词： image modalities depth face detection thermal face detection datasets modalities comparison deep learning

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Low-Cost Hardware-Accelerated vision-Based Depth Perception for Real-Time applications

Low-Cost Hardware-Accelerated Vision-Based Depth Perception ...

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International conference on Computer vision and machine Intelligence, CVMI 2022

作者： Aditya, N.G. Dhruval, P.B. Shylaja, S.S. Katharguppe, Srinivas Department of Computer Science PES University Karnataka Bengaluru560085 India

ISBN: (纸本)9789811978661

Depth estimation and 3D object detection are critical for autonomous systems to gain context of their surroundings. In recent times, compute capacity has improved tremendously, enabling computer vision and AI on the edge. In this paper, we harness the power of CUDA and OpenMP to accelerate ELAS (a stereoscopic vision-based disparity calculation algorithm) and 3D projection of the estimated depth while performing object detection and tracking. We also examine the utility of Bayesian inference in achieving real-time object tracking. Finally, we build a drive-by-wire car equipped with a stereo camera setup to test our system in the real world. The entire system has been made public and easily accessible through a Python module. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stereo image processing

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