The recognition of rice images by UAV based on capsule network

Li, Yu; Qian, Meiyu; Liu, Pengfeng; Cai, Qi; Li, Xiaoying; Junwen, Guo; Yan, Huan; Yu, Fengyuan; Yuan, Kun; Yu, Jian; Qin, Luman; Liu, Hongxin; Wu, Wan Yu; Peiyun, Xiao; Zhou, Ziwei

doi:10.1007/s10586-018-2482-7

Cited by 51 publications

(19 citation statements)

References 17 publications

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“…Although CapsNets were just proposed very recently, they have already been successfully applied in many fields (Afshar et al, 2018;Kumar, 2018;Lalonde and Bagci, 2018;Li et al, 2018;Liu et al, 2018;Mobiny and Van Nguyen, 2018;Qiao et al, 2018;Zhao et al, 2018;Peng et al, 2019). Among these applications, majorities are related to image recognition.…”

Section: Discussionmentioning

confidence: 99%

“…Kumar (2018) proposed a novel method for traffic sign detection using a CapsNet that achieved outstanding performance, the input of which was traffic sign images. Li et al (2018) built a CapsNet to recognize rice composites from unmanned aerial vehicle (UAV) images. This is understandable because CapsNets were originally developed to overcome the defects associated with image recognition in the traditional deep learning networks.…”

Section: Discussionmentioning

confidence: 99%

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Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

et al. 2020

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Capsule networks (CapsNets), a new class of deep neural network architectures proposed recently by Hinton et al., have shown a great performance in many fields, particularly in image recognition and natural language processing. However, CapsNets have not yet been applied to drug discovery-related studies. As the first attempt, we in this investigation adopted CapsNets to develop classification models of hERG blockers/ nonblockers; drugs with hERG blockade activity are thought to have a potential risk of cardiotoxicity. Two capsule network architectures were established: convolution-capsule network (Conv-CapsNet) and restricted Boltzmann machine-capsule networks (RBM-CapsNet), in which convolution and a restricted Boltzmann machine (RBM) were used as feature extractors, respectively. Two prediction models of hERG blockers/nonblockers were then developed by Conv-CapsNet and RBM-CapsNet with the Doddareddy's training set composed of 2,389 compounds. The established models showed excellent performance in an independent test set comprising 255 compounds, with prediction accuracies of 91.8 and 92.2% for Conv-CapsNet and RBM-CapsNet models, respectively. Various comparisons were also made between our models and those developed by other machine learning methods including deep belief network (DBN), convolutional neural network (CNN), multilayer perceptron (MLP), support vector machine (SVM), k-nearest neighbors (kNN), logistic regression (LR), and LightGBM, and with different training sets. All the results showed that the models by Conv-CapsNet and RBM-CapsNet are among the best classification models. Overall, the excellent performance of capsule networks achieved in this investigation highlights their potential in drug discoveryrelated studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

et al. 2020

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“…LaLonde and Bagci [ 37 ] expanded the use of CapsNets to the task of object segmentation for the first time and achieved a promising segmentation accuracy. Li et al [ 38 ] built a CapsNet to recognize rice composites from UAV images. Qiao et al [ 39 ] captured the high-level features using a CapsNet to reconstruct image stimuli from human fMRI, achieving higher accuracy than all existing state-of-the-art methods.…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Deng

Chen

et al. 2018

Sensors

118

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Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

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“…school dense residential Recently, the advent of the capsule network (CapsNet) [36], which is a novel architecture to encode the properties and spatial relationship of the features in an image and is a more effective image recognition algorithm, shows encouraging results on image classification. Although the CapsNet is still in its infancy [37], it has been successfully applied in many fields [38][39][40][41][42][43][44][45][46][47][48][49] in recent years, such as brain tumor classification, sound event detection, object segmentation, and hyperspectral image classification. The CapsNet uses a group of neurons as a capsule to replace a neuron in the traditional neural network.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Scene Classification Using CNN-CapsNet

2019

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Remote sensing image scene classification is one of the most challenging problems in understanding high-resolution remote sensing images. Deep learning techniques, especially the convolutional neural network (CNN), have improved the performance of remote sensing image scene classification due to the powerful perspective of feature learning and reasoning. However, several fully connected layers are always added to the end of CNN models, which is not efficient in capturing the hierarchical structure of the entities in the images and does not fully consider the spatial information that is important to classification. Fortunately, capsule network (CapsNet), which is a novel network architecture that uses a group of neurons as a capsule or vector to replace the neuron in the traditional neural network and can encode the properties and spatial information of features in an image to achieve equivariance, has become an active area in the classification field in the past two years. Motivated by this idea, this paper proposes an effective remote sensing image scene classification architecture named CNN-CapsNet to make full use of the merits of these two models: CNN and CapsNet. First, a CNN without fully connected layers is used as an initial feature maps extractor. In detail, a pretrained deep CNN model that was fully trained on the ImageNet dataset is selected as a feature extractor in this paper. Then, the initial feature maps are fed into a newly designed CapsNet to obtain the final classification result. The proposed architecture is extensively evaluated on three public challenging benchmark remote sensing image datasets: the UC Merced Land-Use dataset with 21 scene categories, AID dataset with 30 scene categories, and the NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that the proposed method can lead to a competitive classification performance compared with the state-of-the-art methods.

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The recognition of rice images by UAV based on capsule network

Cited by 51 publications

References 17 publications

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Remote Sensing Image Scene Classification Using CNN-CapsNet

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