PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification

Tang, Chaohui; Zhu, Qingxin; Wu, Wenjun; Huang, Wenlin; Hong, Chaoqun; Niu, Xinjian

doi:10.1155/2020/1245924

Cited by 24 publications

(22 citation statements)

References 14 publications

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“…Data enhancement aims to expand the capacity of the data set through randomly flipping, rotating and cutting images, and randomly setting their brightness, contrast, saturation and chroma [22]. It is worth noting that data enhancement is to randomly process the original data in each epoch of training, which is different from the previous data amplification.…”

Section: ) Data Preprocessingmentioning

confidence: 99%

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

Yang

et al. 2021

IEEE Access

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Electrical discharge is a sign of insulation damage of power equipment and an essential part of power inspection. For the recognition of plasma discharge patterns, the accuracy rate of traditional manual inspection relies heavily on the experience of the staff, and misjudgment is inevitable. The emerging method based on manual feature extraction of discharge signals also has its limitations. Thus, a high-accurate recognition method based on convolutional neural network (CNN) and visible images is proposed in this paper. Under the deep learning framework-TensorFlow, CNN model is established and pre-trained weights are loaded through transfer learning. The visible image dataset used for training and evaluation includes four discharge patterns: arc discharge, corona discharge, creeping discharge and plasma jet. In data processing, data amplification and data enhancement are used to expand the sample. Besides, typical network models with fully connected layers are adopted and compared. Three optimization methods of ReLU, BN and Dropout are applied to solve the overfitting problem, and the corresponding effects are discussed in the model of VGG16 and ResNet152. The test accuracy of the optimized VGG16 reaches 99.6% without overfitting, which indicates the promising potential in the recognition of plasma discharge patterns in power inspection. INDEX TERMS electrical discharge, plasma discharge patterns, visible images, image recognition, convolutional neural network, transfer learningThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: ) Data Preprocessingmentioning

confidence: 99%

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

Yang

et al. 2021

IEEE Access

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“…To provide sufficient data for model training, 12,000 augmented images of non-wheat ear, one wheat ear, two wheat ears, and three wheat ears were produced by randomly cutting, flipping, rotating, and adjusting the brightness of the original image [29][30][31]. The expanded data set was divided into a training and test set, and each class included 11,000 training sets and 1000 test sets.…”

Section: Data Set Constructionmentioning

confidence: 99%

Wheat ear counting using K-means clustering segmentation and convolutional neural network

Yin

et al. 2020

Plant Methods

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Background: Wheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices. The segmented data set was constructed by creating four categories of image labels: non-wheat ear, one wheat ear, two wheat ears, and three wheat ears, which was then was sent into the convolution neural network (CNN) model for training and testing to reduce the complexity of the model. Results: The recognition accuracy of non-wheat, one wheat, two wheat ears, and three wheat ears were 99.8, 97.5, 98.07, and 98.5%, respectively. The model R 2 reached 0.96, the root mean square error (RMSE) was 10.84 ears, the macro F1-score and micro F1-score both achieved 98.47%, and the best performance was observed during late grainfilling stage (R 2 = 0.99, RMSE = 3.24 ears). The model could also be applied to the UAV platform (R 2 = 0.97, RMSE = 9.47 ears). Conclusions: The classification of segmented images as opposed to target recognition not only reduces the workload of manual annotation but also improves significantly the efficiency and accuracy of wheat ear counting, thus meeting the requirements of wheat yield estimation in the field environment.

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“…Водночас, спираючись на характеристики знімальної апаратури та умови знімання, які входять до метаданих знімання за допомогою спеціальної процедури моделювання знімання наявні в базі графічні еталони перетворюються на модельні зображення тієї ж самої знімальної апаратури, якою отримано вхідне зображення -еталонні сегменти. Відтепер стає можливим використання нейронної мережі розпізнавання зображення малорозмірного об'єкта підвищеної розрізненності серед модельних зображень еталонних сегментів (Tang et al, 2020), за результатами якого визначається той з графічних еталонів, що відповідає малорозмірному об'єкту -ідентифікований еталон. Ідентифікацію виконано.…”

Section: методunclassified

Neural network technology adaptation to the small-size objects identification in satellite images of insufficient resolution within the graphic reference images database

Stankevich

Maslenko²,

Andronov³

2020

UJRS

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A novel flowchart for small-size objects identification in satellite images of insufficient resolution within the graphic reference images database using neural network technology based on compromise contradiction, i.e. simultaneously the resolution enhancement of the object segment of input image and the resolution reduction of the reference image to joint resolution through the simulation of the imaging system has been proposed. This is necessary due to a significant discrepancy between the resolutions of the input image and the graphic reference images used for identification. The required level of resolution enhancement for satellite images, as a rule, is unattainable, and a significant coarsening of reference images is undesirable because of identification errors. Therefore, a certain intermediate spatial resolution is used for identification, which, on the one hand, can be obtained, and on the other the loss of information contained in the reference image is still acceptable. The intermediate resolution is determined by simulating the process of image acquisition with satellite imaging system. To facilitate such simulation, it is advisable to perform it in the frequency domain, where the advanced Fourier analysis is available and, as a rule, all the necessary transfer properties of the links of image formation chain are known. Three main functional elements are engaged for identification: an artificial neural network for the resolution enhancement of input images, a module of frequency-domain simulating of the graphical reference satellite imaging and an artificial neural network for comparing the enhanced object segment with the reference model images. The feasibility of the described approach is demonstrated by the example of successful identification of the sea vessel image in the SPOT-7 satellite image. Currently, the works are under way to compare the performance of a neural network platforms variety for small-size objects identification in satellite images aa well as to assess achievable accuracy.

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PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification

Cited by 24 publications

References 14 publications

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

Recognition of Plasma Discharge Patterns Based on CNN and Visible Images

Wheat ear counting using K-means clustering segmentation and convolutional neural network

Neural network technology adaptation to the small-size objects identification in satellite images of insufficient resolution within the graphic reference images database

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