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Research and design of a machine vision-based silk cocoon quality inspection system 10

Research and design of a machine vision-based silk cocoon qu...

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10th ieee International conference on Cyber Security and Cloud Computing and 9th ieee International conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yang, Chengjun Peng, Jansheng Cai, Jiahong Tang, Yuna Zhou, Ling Yan, Yaosheng Hechi University School of Artificial Intelligence and Manufacturing YiZhou City China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan City China

ISBN: (纸本)9798350312461

Silk cocoon is one of the critical textile raw materials, and its quality has a significant impact on production and processing. In view of the problems such as time-consuming, labor-intensive, and low efficiency in the existing silk cocoon quality inspection methods, this paper proposes a machine vision-based silk cocoon quality inspection system. For different types of silk cocoons, multiple machine vision techniques are used for image processing.and feature extraction. The quality characteristics of silk cocoons are discriminated and analyzed by machine learning algorithms to achieve automatic detection of the cocoon quality. Experimental results show that the proposed system has high accuracy and fast detection speed and can meet the requirements of automated detection in the silk cocoon production process. © 2023 ieee.

关键词： Feature extraction

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Fast Camera image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Fast Camera Image Denoising on Mobile GPUs with Deep Learnin...

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ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Ignatov, Andrey Byeoung-Su, Kim Timofte, Radu Pouget, Angeline Song, Fenglong Li, Cheng Xiao, Shuai Fu, Zhongqian Maggioni, Matteo Huang, Yibin Cheng, Shen Lu, Xin Zhou, Yifeng Chen, Liangyu Liu, Donghao Zhang, Xiangyu Fan, Haoqiang Sun, Jian Liu, Shuaicheng Kwon, Minsu Lee, Myungje Yoo, Jaeyoon Kang, Changbeom Wang, Shinjo Huang, Bin Zhou, Tianbao Liu, Shuai Lei, Lei Feng, Chaoyu Huang, Liguang Lei, Zhikun Chen, Feifei Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Samsung Elect Suwon South Korea AI Witchlabs Zollikerberg Switzerland Huawei Noahs Ark Lab Hong Kong Peoples R China Megvii Beijing Peoples R China ENERZAi Seoul South Korea Xiaomi AI Lab Beijing Peoples R China North China Univ Technol Beijing Peoples R China Xiaomi Inc Beijing Peoples R China Goodix Technol Algorithm Dept Shenzhen Peoples R China

ISBN: (纸本)9781665448994

image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing.480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Runtime image resolution Neural networks Graphics processing.units Mobile handsets pattern recognition Noise measurement

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MuIT: An End-to-End Multitask Learning Transformer

MuIT: An End-to-End Multitask Learning Transformer

引用

2022 ieee/CVF conference on computer Vision and pattern recognition, CVPR 2022

作者： Bhattacharjee, Deblina Zhang, Tong Susstrunk, Sabine Salzmann, Mathieu School of Computer and Communication Sciences Epfl Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads. At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks. We evaluate our model on several multitask benchmarks, showing that our MulT framework outperforms both the state-of-the art multitask convolutional neural network models and all the respective single task transformer models. Our experiments further highlight the benefits of sharing attention across all the tasks, and demonstrate that our MulT model is robust and generalizes well to new domains. Our project website is at https://***/MulT/. © 2022 ieee.

关键词： Heart image segmentation Computational modeling image edge detection Semantics Estimation Predictive models

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Natural image matting based on image inpainting 2

Natural image matting based on image inpainting

引用

2nd International conference on computer Graphics, image and Virtualization, ICCGIV 2022

作者： Zhang, Yuan Tan, Mian Zhou, Zhulian Yang, Yuan Liang, Yihui Feng, Fujian Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang550025 China University of Electronic Science and Technology China School of Computer Science Zhongshan528400 China

ISBN: (纸本)9781665492508

Deep image matting is a hot problem with applications in computer vision and image processing. It has been widely used in image composition, film production and video editing etc. The current matting method based on image inpainting uses a deep neural network to inpaint the foreground target and background region to further improve the accuracy of alpha matte. However, when the trimap contains large unknown regions, the excessive inpainting of foreground and background produce a lot of redundant information, which leads to a degradation of alpha matte quality. Therefore, to address this issue, a natural image matting method based on image inpainting is designed. This method involves the refinement process for trimap, which improves the quality of the trimap, enlarges the foreground and background regions providing additional information for image matting. Extensive experimental results on the composition-1k dataset demonstrate that the presented method provide high-quality alpha mattes not only in the case that the trimap contains small unknown regions, but also in the case that the trimap contains large unknown regions. © 2022 ieee.

关键词： Deep neural networks

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Gas/Odor Qualitative and Quantitative Estimation Methodology for a Bionic Electronic Nose

Gas/Odor Qualitative and Quantitative Estimation Methodology...

引用

2023 ieee Biomedical Circuits and Systems conference, BioCAS 2023

作者： Zhang, Wenwen Zhang, Zhengyuan Zhongzhiguang, Lu Lin, Zhiping Zheng, Yuanjin Nanyang Technological University School of Electrical and Electronics Engineering Singapore Singapore University of Shanghai for Science and Technology School of Science School of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798350300260

The electronic nose (E-nose) efficiently detects the composition and concentration of gases within a remarkably short span, enabling the identification of volatile metabolites emitted by the human body. However, Traditional methods for both qualitative and quantitative gas estimation face challenges in efficiently employing real-time signals from electronic noses, thereby resulting in imprecise online gas qualitative and quantitative estimation. To address this limitation, our research introduces a cascaded approach that integrates two models: the Muti-scale Wavelet Coefficient image-Capsule Network (MWCI-CapsNet) for gas identification and the weighted real-time signal-cosformer (weighted-cosformer) for concentration estimation. This approach directly utilizes the real-time signal data from the E-nose as input, eliminating the need for signal pre-processing. By conducting experiments aimed at identifying CO, H2, and mixed gas compositions of CO and H2 using our custom-developed bionic electronic nose, our proposed MWCI-CapsNet model achieves a remarkable accuracy approaching 100% in gas recognition while employing a reduced number of annotated data samples. Furthermore, the weighted-cosformer model demonstrates superior performance when contrasted with the conventional gas quantitative estimation model. © 2023 ieee.

关键词： Capsule Network electronic nose gas identification wavelet coefficient

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A Drug Box recognition System Based on Deep Learning and Cloud-Edge Collaboration for Pharmacy Automation 29

A Drug Box Recognition System Based on Deep Learning and Clo...

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29th ieee International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhu, Honglei Dong, Anming Yu, Jiguo School of Computer Science and Technology Jinan China Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Drug box recognition is an integral part of the pharmacy automation system (PAS), which checks for discrepancies between the drugs provided by the system and the doctor's prescription with the help of modern image recognition techniques. Although deep learning has facilitated the development of pharmacy automation systems, practical applications still face challenges. First, pharmacies update drugs frequently, and many pharmacies across the country are trained independently, which requires consuming massive computational resources. Secondly, the equipment performance of pharmacies is poor, and it's hard to guarantee the real-time and accuracy of recognition. Therefore, we propose a cloud-side collaborative drug box recognition system. When new drugs are added, the new model is trained in the cloud and uniformly distributed to each edge through this architecture. In terms of real-time recognition, an improved lightweight YOLOv5 model is added to the edge part, and a text recognition module is deployed in the cloud to validate the recognition results at the edge. This cloud-edge collaboration architecture not only ensures the accuracy of recognition, but also reduces the burden on the compute edge, ensures the real-time *** results show that the proposed cloud-edge architecture reduces the recognition latency by 37.18% and achieves a precision of 97.47%. The improved YOLOv5 model reduces GFLOPs by 45% while achieving a 1.6% precision improvement. © 2023 ieee.

关键词： Cloud-Edge Collaboration image recognition Pharmacy Automation Text recognition

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Brain graph synthesis for multi-site autism spectrum disorder identification

Brain graph synthesis for multi-site autism spectrum disorde...

引用

2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Li, Wenqi Huang, Huifang School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510666351

Recently, machine learning attracts more attention on autism spectrum disorder (ASD) identification based on resting-state functional magnetic resonance imaging (rs-fMRI). Most studies on ASD identification use rs-fMRI data from multiple imaging sites to increase sample size, but they suffer from the data heterogeneity among sites. Besides, most ASD identification studies use features simply extracted from brain connections, ignoring the topological structure of brain. To address these issues, we propose a brain graph synthesis model based on generative adversarial network (GAN), which transforms data from the source domain to the target domain, solving the data heterogeneity among sites. Specifically, the generator and discriminator are designed for graph structured data and propose topological losses to construct the cycle consistency loss for maintaining the original topological structure of the reconstructed brain graph. We carry out experiments on six tasks using the open database Autism Brain Imaging Data Exchange (ABIDE) for ASD identification. Experimental findings demonstrate that our model can solve the problem of data heterogeneity effectively and achieve satisfying performance on multi-site ASD identification. © 2023 SPIE.

关键词： Generative adversarial networks

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CoCV: Heterogeneous Processors Collaboration Mechanism for End-to-End Execution of Intelligent computer Vision Tasks on Mobile Devices 29

CoCV: Heterogeneous Processors Collaboration Mechanism for E...

引用

29th ieee International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wan, Ye Liu, Mengyang Li, Guangtong Dong, Fang Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350330717

Object detection, image classification, and various other computer vision tasks have become prevalent on mobile devices. These computer vision tasks are typically executed with three stages: pre-processing. inference, and post-processing. Mobile SoC, serving as the computing unit on mobile devices, typically consists of heterogeneous processors like CPU, GPU, and NPU. However, during the execution of a computer vision task, current available frameworks only achieve the parallelism of CPU and GPU in the inference stage. While during pre- and post-processing. only CPU is used, leaving GPU and NPU on the SoC to be idle. For mobile applications that require low latency, the overhead of pre-processing.and post-processing.stages often account for more than 50% of the total latency, which becoming a performance bottleneck of the entire task. To reduce latency, it is imperative to fully utilize the idle heterogeneous processors (GPU, NPU) on the SoC and achieve heterogeneous processors parallelism in all three stages during execution. In this paper, we propose CoCV, a heterogeneous processor parallel computing system for computer vision tasks on mobile devices. In CoCV, we are the first to build an image processing.operator library for heterogeneous parallel computing on mobile devices. Besides, we design a task allocation scheduling algorithm to guide the partitioning of processing.tasks during execution, which ensuring a relatively balanced workload between different processors. A cross-stage operator chaining technique is also proposed to reduce the data sharing overhead among different processors during execution. We build a prototype system and evaluate it with different computer vision tasks. The results show up to 33% latency reduction for end-to-end tasks and 2.32× speedup compared with the current best solution. © 2023 ieee.

关键词： CPU&GPU Co-execution Heterogeneous Computing Mobile devices

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Pseudo-label based and transformation-consistent self-ensembling model for underwater life images

Pseudo-label based and transformation-consistent self-ensemb...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Huang, Shan Luo, Xiaonan Lu, Jiahao Huang, Chao Xu, Chengpei Yuan, Tianran School of Computer Science and Information Security Guilin University of Electronics Technology No. 1 Jinji Road Guilin541004 China

ISBN: (纸本)9781510666351

With the continuous development of underwater vision technology, high-precision object segmentation has received more attention from scholars. Due to the complexity of the underwater environment, the segmentation of underwater objects has become a new challenge. In order to alleviate such problems, this paper proposes a semi-supervised segmentation model based on pseudo-labels and transformation-consistent(PSTM). Due to the complexity of underwater images, PSTM preprocesses underwater biological images through an image enhancement module. A semi-supervised segmentation network with pseudo-labels and transformation-consistent is then used to obtain a more robust segmentation result. The method in this paper achieves better performance on the public underwater biological dataset DUT-USEG. © 2023 SPIE.

关键词： Deep neural networks

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Semi-auto labeling device for 6Dof object pose estimation

Semi-auto labeling device for 6Dof object pose estimation

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2022 International conference on image, Signal processing. and pattern recognition, ISPP 2022

作者： Liao, Meizhen Wei, Wei Zhang, Xiaojie Long, Yuzhong Li, Hanxi School of Computer and Information Engineering Jiangxi Normal University 330022 China SpritAR Shanghai200000 China

ISBN: (纸本)9781510654846

In this work, we propose to generate the supervised training dataset for 6-DoF object pose estimation algorithm, with trivial human labor. A semi-auto labelling board is designed so that its pose can be estimated accurately with a learned deep model. For a training sequence with more than 1000 frames, one only needs to manually mark the key points of the object for 2-5 frames and the ground-truth pose of the object can be calculated automatically for the whole sequence. In the experiment, we prove that the state-of-the-art pose estimation method can be trained well with only 20-50 human-labelled images and the yielded model performs better than the model learned based on the manually labelled dataset with more than 800 images. © 2022 SPIE.

关键词： computer vision

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