ASSESSING THE SEMANTIC SIMILARITY OF IMAGES OF SILK FABRICS USING CONVOLUTIONAL NEURAL NETWORKS

Abstract. This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (http://silknow.eu/), two variants of training were developed, one based on a Siamese CNN and one based on a triplet architecture. We propose different definitions of similarity and different loss functions for both training strategies, some of them also allowing the use of incomplete information about the training data. We assess the quality of the trained model by using the learned image features in a k-NN classification. We achieve overall accuracies of 93–95% and average F1-scores of 87–92%.

Download Full-text

Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

Applied Sciences ◽

10.3390/app10062104 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2104

Author(s):

Michał Tomaszewski ◽

Paweł Michalski ◽

Jakub Osuchowski

Keyword(s):

Neural Network ◽

Neural Networks ◽

Object Detection ◽

Convolutional Neural Network ◽

Deep Neural Networks ◽

Detection Efficiency ◽

Training Data ◽

Training Dataset ◽

Training Set ◽

Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

Download Full-text

Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

Remote Sensing ◽

10.3390/rs13193859 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3859

Author(s):

Joby M. Prince Czarnecki ◽

Sathishkumar Samiappan ◽

Meilun Zhou ◽

Cary Daniel McCraine ◽

Louis L. Wasson

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Quality ◽

Convolutional Neural Network ◽

Precision Agriculture ◽

Edge Computing ◽

Training Data ◽

Learning Approaches ◽

Sky Conditions

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.

Download Full-text

Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

Download Full-text

Quality Index of Supervised Data for Convolutional Neural Network-Based Localization

Applied Sciences ◽

10.3390/app9101983 ◽

2019 ◽

Vol 9 (10) ◽

pp. 1983 ◽

Cited By ~ 2

Author(s):

Seigo Ito ◽

Mineki Soga ◽

Shigeyoshi Hiratsuka ◽

Hiroyuki Matsubara ◽

Masaru Ogawa

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Quality Index ◽

Single Photon ◽

Automated Guided Vehicles ◽

Training Data ◽

Supervised Training ◽

Localization Performance ◽

Imaging Sensor

Automated guided vehicles (AGVs) are important in modern factories. The main functions of an AGV are its own localization and object detection, for which both sensor and localization methods are crucial. For localization, we used a small imaging sensor named a single-photon avalanche diode (SPAD) light detection and ranging (LiDAR), which uses the time-of-flight principle and arrays of SPADs. The SPAD LiDAR works both indoors and outdoors and is suitable for AGV applications. We utilized a deep convolutional neural network (CNN) as a localization method. For accurate CNN-based localization, the quality of the supervised data is important. The localization results can be poor or good if the supervised training data are noisy or clean, respectively. To address this issue, we propose a quality index for supervised data based on correlations between consecutive frames visualizing the important pixels for CNN-based localization. First, the important pixels for CNN-based localization are determined, and the quality index of supervised data is defined based on differences in these pixels. We evaluated the quality index in indoor-environment localization using the SPAD LiDAR and compared the localization performance. Our results demonstrate that the index correlates well to the quality of supervised training data for CNN-based localization.

Download Full-text

Plastic Gasket Defect Detection Based on Transfer Learning

Scientific Programming ◽

10.1155/2021/5990020 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xieyi Chen ◽

Dongyun Wang ◽

Jinjun Shao ◽

Jun Fan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Defect Detection ◽

Data Augmentation ◽

Surface Defects ◽

Visual Detection ◽

Training Data

To automatically detect plastic gasket defects, a set of plastic gasket defect visual detection devices based on GoogLeNet Inception-V2 transfer learning was designed and established in this study. The GoogLeNet Inception-V2 deep convolutional neural network (DCNN) was adopted to extract and classify the defect features of plastic gaskets to solve the problem of their numerous surface defects and difficulty in extracting and classifying the features. Deep learning applications require a large amount of training data to avoid model overfitting, but there are few datasets of plastic gasket defects. To address this issue, data augmentation was applied to our dataset. Finally, the performance of the three convolutional neural networks was comprehensively compared. The results showed that the GoogLeNet Inception-V2 transfer learning model had a better performance in less time. It means it had higher accuracy, reliability, and efficiency on the dataset used in this paper.

Download Full-text

Image classification of different clove (Syzygium aromaticum) quality using deep learning method with convolutional neural network algorithm

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/905/1/012018 ◽

2021 ◽

Vol 905 (1) ◽

pp. 012018

Author(s):

I Y Prayogi ◽

Sandra ◽

Y Hendrawan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Test Data ◽

Training Data ◽

Syzygium Aromaticum ◽

Learning Method ◽

Validation Data ◽

Reading Accuracy

Abstract The objective of this study is to classify the quality of dried clove flowers using deep learning method with Convolutional Neural Network (CNN) algorithm, and also to perform the sensitivity analysis of CNN hyperparameters to obtain best model for clove quality classification process. The quality of clove as raw material in this study was determined according to SNI 3392-1994 by PT. Perkebunan Nusantara XII Pancusari Plantation, Malang, East Java, Indonesia. In total 1,600 images of dried clove flower were divided into 4 qualities. Each clove quality has 225 training data, 75 validation data, and 100 test data. The first step of this study is to build CNN model architecture as first model. The result of that model gives 65.25% reading accuracy. The second step is to analyze CNN sensitivity or CNN hyperparameter on the first model. The best value of CNN hyperparameter in each step then to be used in the next stage. Finally, after CNN hyperparameter carried out the reading accuracy of the test data is improved to 87.75%.

Download Full-text

Automatic vegetation identification in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests

10.1101/351643 ◽

2018 ◽

Author(s):

Shuntaro Watanabe ◽

Kazuaki Sumi ◽

Takeshi Ise

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Environmental Science ◽

Google Earth ◽

Image Resolution ◽

Training Data ◽

Identification Accuracy ◽

Automatic Identification

ABSTRACTClassifying and mapping vegetation are very important tasks in environmental science and natural resource management. However, these tasks are not easy because conventional methods such as field surveys are highly labor intensive. Automatic identification of target objects from visual data is one of the most promising ways to reduce the costs for vegetation mapping. Although deep learning has become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation still is considered difficult. In this paper, we investigated the potential for adapting the chopped picture method, a recently described protocol for deep learning, to detect plant communities in Google Earth images. We selected bamboo forests as the target. We obtained Google Earth images from three regions in Japan. By applying the deep convolutional neural network, the model successfully learned the features of bamboo forests in Google Earth images, and the best trained model correctly detected 97% of the targets. Our results show that identification accuracy strongly depends on the image resolution and the quality of training data. Our results also highlight that deep learning and the chopped picture method can potentially become a powerful tool for high accuracy automated detection and mapping of vegetation.

Download Full-text

Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation

Remote Sensing ◽

10.3390/rs12203421 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3421 ◽

Cited By ~ 1

Author(s):

Tiange Liu ◽

Qiguang Miao ◽

Pengfei Xu ◽

Shihui Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Practical Method ◽

Training Data ◽

Topographic Map ◽

Watershed Transform ◽

Testing Dataset ◽

Map Segmentation ◽

Short Run

Motivated by applications in topographic map information extraction, our goal was to discover a practical method for scanned topographic map (STM) segmentation. We present an advanced guided watershed transform (AGWT) to generate superpixels on STM. AGWT utilizes the information from both linear and area elements to modify detected boundary maps and sequentially achieve superpixels based on the watershed transform. With achieving an average of 0.06 on under-segmentation error, 0.96 on boundary recall, and 0.95 on boundary precision, it has been proven to have strong ability in boundary adherence, with fewer over-segmentation issues. Based on AGWT, a benchmark for STM segmentation based on superpixels and a shallow convolutional neural network (SCNN), termed SSCNN, is proposed. There are several notable ideas behind the proposed approach. Superpixels are employed to overcome the false color and color aliasing problems that exist in STMs. The unification method of random selection facilitates sufficient training data with little manual labeling while keeping the potential color information of each geographic element. Moreover, with the small number of parameters, SCNN can accurately and efficiently classify those unified pixel sequences. The experiments show that SSCNN achieves an overall F1 score of 0.73 on our STM testing dataset. They also show the quality of the segmentation results and the short run time of this approach, which makes it applicable to full-size maps.

Download Full-text

Convolutional Neural Network

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

10.4018/978-1-7998-3479-3.ch002 ◽

2021 ◽

pp. 12-26

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Download Full-text

Comparative results of the classification accuracy of MSTAR dataset radar images by convolutional neural networks with different architectures.

Journal of Radio Electronics ◽

10.30898/1684-1719.2021.11.14 ◽

2021 ◽

Vol 2021 (11) ◽

Author(s):

I.F. Kupryashkin ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Training Data ◽

Convolutional Networks ◽

Radar Images ◽

Neural Network Classification ◽

Learning Technique ◽

Comparative Results

The results of MSTAR objects ten-classes classification using a VGG-type deep convolutional neural network with eight convolutional layers are presented. The maximum accuracy achieved by the network was 97.91%. In addition, the results of the MobileNetV1, Xception, InceptionV3, ResNet50, InceptionResNetV2, DenseNet121 networks, prepared using the transfer learning technique, are presented. It is shown that in the problem under consideration, the use of the listed pretrained convolutional networks did not improve the classification accuracy, which ranged from 93.79% to 97.36%. It has been established that even visually unobservable local features of the terrain background near each type of object are capable of providing a classification accuracy of about 51% (and not the expected 10% for a ten-alternative classification) even in the absence of object and their shadows. The procedure for preparing training data is described, which ensures the elimination of the influence of the terrain background on the result of neural network classification.

Download Full-text