A way of incorporating landmark information matrix QR to affect the global path planning algorithm is proposed for the use of landmarks in robot localization and navigation. It is also investigated how this method aff...
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With good mobility and flexibility,mobile manipulators have shown broad applications in construction *** position(BP)planning,which refers to the robot autonomously determining its working station in the environment,i...
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With good mobility and flexibility,mobile manipulators have shown broad applications in construction *** position(BP)planning,which refers to the robot autonomously determining its working station in the environment,is an important technique for mobile manipulators when performing the construction assembly task,especially in a large-scale construction ***,the BP planning process is tedious and time-consuming for a human worker to carry ***,to improve the efficiency of construction assembly tasks,a novel BP planning method is proposed in this paper,which can lead to appropriate BPs and minimize the number of BPs at the same ***,the feasible BP regions are generated based on the grid division and the variable workspace of the mobile ***,the positioning uncertainties of the mobile manipulator are considered in calculating the preferred BP areas using ***,a set coverage optimization model is established to obtain the minimum number of BPs using an optimization algorithm according to the greedy *** simulated experiment based on a 9-degree of free(DoF)mobile manipulator has been *** results illustrated that the time for BP planning was significantly reduced and the number of BPs was reduced by 63.41%compared to existing manual planning,which demonstrated the effectiveness of the proposed method.
The success of Graph Neural Networks (GNNs) in graph classification has heightened interest in explainable GNNs, particularly through graph rationalization. This method aims to enhance GNNs explainability by identifyi...
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To address the problem of conflicting segmentation accuracy and model parametric numbers in many lumbar MR image disc segmentation methods, we propose a lightweight segmentation pipeline named Lite-snake for lumbar MR...
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Citrus rind color is a good indicator of fruit development,and methods to monitor and predict color transformation therefore help the decisions of crop management practices and harvest *** work presents the complete w...
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Citrus rind color is a good indicator of fruit development,and methods to monitor and predict color transformation therefore help the decisions of crop management practices and harvest *** work presents the complete workflow to predict and visualize citrus color transformation in the orchard featuring high accuracy and fidelity.A total of 107 sample Navel oranges were observed during the color transformation period,resulting in a dataset containing 7,535 citrus images.A framework is proposed that integrates visual saliency into deep learning,and it consists of a segmentation network,a deep mask-guided generative network,and a loss network with manually designed loss ***,the fusion of image features and temporal information enables one single model to predict the rind color at different time intervals,thus effectively shrinking the number of model *** semantic segmentation network of the framework achieves the mean intersection over a union score of 0.9694,and the generative network obtains a peak signal-to-noise ratio of 30.01 and a mean local style loss score of 2.710,which indicate both high quality and similarity of the generated images and are also consistent with human *** ease the applications in the real world,the model is ported to an Android-based application for mobile *** methods can be readily expanded to other fruit crops with a color transformation *** dataset and the source code are publicly available at GitHub.
Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely ...
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Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.
作者：
Jian, CaiqingQin, YongbinWang, LihuiYe, ChenCheng, XinyuGuizhou University
Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China
Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constr...
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Many state-of-the-art low-light image enhancement techniques now suffer from issues like color distortion, detail blurring, and the halo effect, hindering their ability to produce visual effects. This paper presents a...
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Electroencephalogram (EEG) plays a crucial role in Brain-computer interface (BCI) research by recording brain electrical activity, allowing direct communication with external devices without relying on muscles or peri...
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