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Leaf vein segmentation with self-supervision

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 203卷

作者： Li, Lei Hu, Wenzheng Lu, Jiang Zhang, Changshui Tsinghua Univ Inst Artificial Intelligence Beijing Natl Res Ctr Informat Sci & Technol BNRist State Key Lab Intelligent Technol & SystTsinghua Beijing Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China China Marine Dev & Res Ctr CMDRC Beijing Peoples R China Kuaishou Technol Co Ltd Beijing Peoples R China

The leaf vein, often viewed as the fingerprint of the leaf, is an important characteristic used to identify plant species. Leaf vein segmentation aims to extract the vein features and obtain the vein architectures from leaf images. Unlike the general semantic or instance segmentation focusing on the block and object level, the leaf vein segmentation is fine and it focuses on the internal details inside the mesophyll, whose color is indistinguishable, making this task difficult. To tackle this problem, we utilize the particularities of leaf veins, namely continuity and branching, and propose a Confidence Refining Vein Network (CoRE-Net) to segment leaf veins by handling the intersections, breakpoints, and blurred boundaries of veins to enhance segment prediction. Moreover, the proposed network only needs a few labeled samples to warm start, and then the whole network can converge without any annotated labels. Meanwhile, we collect and release the first leaf vein dataset, Leaf Vein Dataset 2021 (LVD2021). The proposed framework achieves mean Intersection over Union at 71.02% and mean Dice at 79.76% on LVD2021, which outperforms its counterparts in different settings, demonstrating the effectiveness of our framework.

关键词： Leaf vein segmentation Computer vision Self-supervision encoder-decoder

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A Transformer-Based Bridge Structural Response Prediction Framework

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SENSORS 2022年第8期22卷 3100页

作者： Li, Ziqi Li, Dongsheng Sun, Tianshu Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China

Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the response values. To address this issue, a Transformer-based bridge structural response prediction framework was proposed in this paper. The framework contains multi-layer encoder modules and attention modules that can precisely capture the history-dependent features in time-series data. The effectiveness of the proposed method was validated with the use of six-month strain response data of a concrete bridge, and the results are also compared with those of the most commonly used Long Short-Term Memory (LSTM)-based structural response prediction framework. The analysis indicated that the proposed method was effective in predicting structural response, with the prediction error less than 50% of the LSTM-based framework. The proposed method can be applied in damage diagnosis and disaster warning of bridges.

关键词： bridge structural response prediction transformer deep learning structural health monitoring encoder-decoder

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Automatic concrete crack segmentation model based on transformer

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AUTOMATION IN CONSTRUCTION 2022年 139卷

作者： Wang, Wenjun Su, Chao Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Routine visual inspection of concrete structures is essential to maintain safe conditions. Therefore, studies of concrete crack segmentation using deep learning methods have been extensively conducted in recent years. However, insufficient performance remains a major challenge in diverse field-inspection scenarios. In this study, a novel SegCrack model for pixel-level crack segmentation is therefore proposed using a hierarchically structured Transformer encoder to output multiscale features and a top-down pathway with lateral connections to progressively up-sample and fuse features from the deepest layer of the encoder. Furthermore, an online hard example mining strategy was adopted to strengthen the detection of hard samples and improve the model performance. The effect of dataset size on the segmentation performance was then investigated. The results indicated that SegCrack achieved a precision, recall, F1 score, and mean intersection over union of 96.66%, 95.46%, 96.05%, and 92.63%, respectively, using the test set.

关键词： Concrete crack Pixel-wise segmentation Visual transformer Self-attention encoder-decoder

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Graph neural network-based visual relationship and multilevel attention for image captioning

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JOURNAL OF ELECTRONIC IMAGING 2022年第5期31卷 053022-053022页

作者： Sharma, Himanshu Srivastava, Swati GLA Univ Dept Comp Engn & Applicat Mathura Uttar Pradesh India

With the remarkable success of the image captioning tasks, visual attention methods have become a vital part of captioning models. However, most attention-based image captioning methods do not consider any relationship among regions, which play a significant role in better image understanding. We proposed an image captioning method based on local relation network using a multilevel attention approach with graph neural network. It not only fully explores the relationship between the object and the image regions but also generates significant and context-based features corresponding to every region in the image. The attention employed in our work enhances the image representation capability of our method by focusing on a given image region and its related image regions. Thus addressing the relevant contextual information, spatial locations, and deep visual features leads to improve caption generation. We verified the effectiveness of the proposed model by conducting extensive experiments on three benchmark datasets: Flickr30k, MSCOCO, and nocaps. The results show the superiority of the proposed method over the existing methods both in quantitative and qualitative manners. Detailed ablation studies are conducted to communicate how each part would contribute to the final performance. (c) 2022 SPIE and IS&T

关键词： graph neural network relation network semantic attention encoder-decoder

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TPET: Two-stage Perceptual Enhancement Transformer Network for Low-light Image Enhancement

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期116卷

作者： Cui, Hengshuai Li, Jinjiang Hua, Zhen Fan, Linwei Shandong Technol & Business Univ Future Intelligent Comp Coinnovat Ctr Shandong Coll & Univ Sch Comp Sci & Technol Yantai Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan Peoples R China

Low-light images captured under low light or backlight conditions can suffer from different types of degradation such as low visibility, strong noise and color distortion. In this paper, to solve the degradation problem of low -light images, we propose Two-stage Perceptual Enhancement Transformer Network(TPET) for Low-light Image Enhancement by combining the advantages of local spatial perception of convolutional neural network and global spatial perception of transformer. The method is generally divided into two stages: feature extraction stage and detail fusion stage. First, in the feature extraction stage, the encoder composed of transformers performs global feature extraction and expands the receptive field. Since the transformer lacks the ability to capture local features, we introduce a perceptual enhancement module (PEM) to improve the interaction of local and global feature information. Second, between the corresponding encoding and decoding blocks in each layer, a feature fusion block (FFB) is introduced to compensate the feature information at different scales to improve the reusability of features and enhance the stability of the network. In addition, between the two stages, the local information features are redistributed and the network supervision capability is improved by introducing a self-calibration module (SCM). In the detail fusion stage, in order to further preserve the details of textural features of the image, we designed a detail enhancement unit (DEU) for recovering high-resolution enhanced images. Through qualitative comparison and quantitative analysis, our method outperforms other low-light image enhancement methods in terms of subjective visual effects and objective metrics values.

关键词： Low-light Image Enhancement Transformer encoder-decoder Attention mechanism

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ARF-Net: An Adaptive Receptive Field Network for breast mass segmentation in whole mammograms and ultrasound images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第PartA期71卷 103178-103178页

作者： Xu, Chunbo Qi, Yunliang Wang, Yiming Lou, Meng Pi, Jiande Ma, Yide Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

UNet adopting an encoder-decoder structure has been used widely in medical image segmentation tasks for its outstanding performance. However, in our work, we find that UNet has the worse segmentation performance of small masses. The reason behind this is that the sizes of receptive fields are limited. In this work, to address this issue, we develop a novel end-to-end model, Adaptive Receptive Field Network (ARF-Net), for the precise breast mass segmentation in whole mammographic images and ultrasound images. ARF-Net composes of an encoder network and a corresponding decoder network, followed by a pixel-wise classifier. In ARF-Net, a Selective Receptive Filed Module (SRFM) is proposed to allocate the suitable sizes of receptive fields to the breast masses of different sizes. SRFM consists of a Multiple Receptive Field Module (MRFM) for generating multiple receptive fields of different sizes and a Multi-Scale Selection Module (MSSM) for selecting the suitable sizes of receptive fields based on the objects' size. The proposed ARF-Net achieves the dice index of 86.1%, 85.75%, and 88.12% on the two mammographic databases (INbreast and CBIS-DDSM) and one ultrasonic database (UDIAT), respectively. Moreover, extensive ablation experiments show that ARF-Net transcends several state-of-the-art segmentation networks, and the developed MSSM exceeds several counterparts.

关键词： Segmentation Mammogram Ultrasound image Deep learning encoder-decoder

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Attention-Based Multistage Fusion Network for Remote Sensing Image Pansharpening

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Zhang, Wanwan Li, Jinjiang Hua, Zhen Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China ICT YANTAD Inst Network Technol Yantai 264005 Peoples R China Shandong Technol & Business Univ Sch Comp Sci & Technol Yantai 264005 Peoples R China

Pansharpening is a significant branch in the field of remote sensing image processing, the goal of which is to fuse panchromatic (PAN) and multispectral (MS) images through certain rules to generate high-resolution MS (HRMS) images. Therefore, how to improve the spatial and spectral resolutions of the fused image is the problem that we need to solve urgently. In this article, a multistage remote sensing image fusion network (MRFNet) is proposed on the basis of in-depth research and exploration on the fusion of the PAN and MS images to obtain a clear fused image that can reflect the ground features more comprehensively and completely. The proposed network consists of three stages that are connected by cross-stage fusion. The first two stages are used to extract the features of the PAN and MS images. The structure of the encoder-decoder and the channel attention module are used to extract the features of the remote sensing image in the channel domain. The third stage is the image reconstruction stage fusing the extracted features with the original image to improve the spatial and spectral resolutions of the fused result. A series of experiments are conducted on the benchmark datasets WorldView II, GF-2, and QuickBird. Qualitative analysis and quantitative comparison show the superiority of MRFNet in visual effects and the values of evaluation indicators.

关键词： Pansharpening Feature extraction Spatial resolution Remote sensing Image resolution Sensors Image reconstruction Attention encoder-decoder multispectral (MS) images panchromatic (PAN) images pansharpening spatial resolution spectral resolution

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A lightweight multi-dimension dynamic convolutional network for real-time semantic segmentation

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 1075520页

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Xu, Chenglong Northeastern Univ Fac Robot Sci & Engn Shenyang Peoples R China Shenyang Siasun Robot & Automat Co Ltd Shenyang Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

Semantic segmentation can address the perceived needs of autonomous driving and micro-robots and is one of the challenging tasks in computer vision. From the application point of view, the difficulty faced by semantic segmentation is how to satisfy inference speed, network parameters, and segmentation accuracy at the same time. This paper proposes a lightweight multi-dimensional dynamic convolutional network (LMDCNet) for real-time semantic segmentation to address this problem. At the core of our architecture is Multidimensional Dynamic Convolution (MDy-Conv), which uses an attention mechanism and factorial convolution to remain efficient while maintaining remarkable accuracy. Specifically, LMDCNet belongs to an asymmetric network architecture. Therefore, we design an encoder module containing MDy-Conv convolution: MS-DAB. The success of this module is attributed to the use of MDy-Conv convolution, which increases the utilization of local and contextual information of features. Furthermore, we design a decoder module containing a feature pyramid and attention: SC-FP, which performs a multi-scale fusion of features accompanied by feature selection. On the Cityscapes and CamVid datasets, LMDCNet achieves accuracies of 73.8 mIoU and 69.6 mIoU at 71.2 FPS and 92.4 FPS, respectively, without pre-training or post-processing. Our designed LMDCNet is trained and inferred only on one 1080Ti GPU. Our experiments show that LMDCNet achieves a good balance between segmentation accuracy and network parameters with only 1.05 M.

关键词： semantic segmentation lightweight network dynamic convolution encoder-decoder multi-dimension convolution

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Multistep short-term wind speed forecasting using transformer

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ENERGY 2022年第PartA期261卷

作者： Wu, Huijuan Meng, Keqilao Fan, Daoerji Zhang, Zhanqiang Liu, Qing Inner Mongolia Univ Technol Coll Energy & Power Engn Aimin St 49 Hohhot 010051 Peoples R China Minist Educ Key Lab Wind Energy & Solar Energy Technol Aimin St 49 Hohhot 010051 Peoples R China Inner Mongolia Autonomous Reg Wind Power Technol & Aimin St 49 Hohhot 010051 Peoples R China Inner Mongolia Univ Coll Elect Informat Engn Coll Rd 235 Hohhot 010021 Peoples R China

Wind power can effectively alleviate the energy crisis. However, its integration into the grid affects power quality and power grid stability. Accurate wind speed prediction is a key factor in the efficient use of wind power. Because of its intermittent and nonstationary nature, wind speed forecasting is difficult, and is the topic of much research, especially long-time multistep forecasts. In this paper, the multistep wind speed prediction problem is regarded as a sequence-to-sequence mapping problem, and a multistep wind speed prediction model based on a transformer is proposed. This model is based on an encoder-decoder architecture, where the encoder generates representations of historical wind speed sequences of any length, the decoder generates arbitrarily long future wind speed sequences, and the encoder and decoder are associated by an attention mechanism. At the same time, the encoder and decoder of Transformer are completely based on a multi-head attention mechanism. For easy modeling, a 1-dimensional original wind speed sequence is transformed to a 16-dimensional sequence by ensemble empirical mode decomposition (EEMD), and the multidimensional wind speed data are directly modeled with Transformer. We trained the model with very large-scale (19 years of data) wind speed data averaged at 10-minute intervals, and performed the evaluation over one-year wind speed data. Results show that our one-step forecast model achieved an average mean absolute error (MAE) and root mean square error (RMSE) of 0.167 and 0.221, respectively. To the best of our knowledge, our 3-, 6-, 12-, and 24-hour multistep forecast model achieves a new state of the art in wind speed forecasting, with respective MAEs of 0.243, 0.290, 0.362, and 0.453, and RMSEs of 0.326, 0.401, 0.513, and 0.651. It is believed that performance can be further improved with better model parameter optimization.

关键词： Wind speed forecasting Transformer EEMD Multistep encoder-decoder

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Text Detection and Recognition in Natural Scenes Based on TWO-DIMENSIONAL Attention 22

Text Detection and Recognition in Natural Scenes Based on TW...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Baicun Guo Computer Science North China Electric Power University China

ISBN: (纸本)9781450397810

In this paper, we improve the natural scene text detection and recognition technology based on 2d attention and encoder-decoder framework. Firstly, the related work of text detection and recognition in different natural view is discussed. Secondly, we work on the basis of encoder-decoder framework and two-dimention module, and improve it through aggregation and hybridisation. Finally, we discussed and analyzed the results,and figured out the possible shortcomings of the model.

关键词： two-dimention attention aggregation encoder-decoder

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