A Detection algorithm based on Convolutional Neural Network

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

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Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

Scientific Reports ◽

10.1038/s41598-021-01170-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lee Ming Jun Melvin ◽

Rajesh Elara Mohan ◽

Archana Semwal ◽

Povendhan Palanisamy ◽

Karthikeyan Elangovan ◽

...

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Urban Environment ◽

Field Trial ◽

Detection Algorithm ◽

Data Set ◽

Detection Model ◽

Inspection Robot ◽

Online Test

AbstractDrain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called ‘Raptor’. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.

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Text Detection on Images using Region-based Convolutional Neural Network

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v4n2y2020.pp40-45 ◽

2020 ◽

Vol 4 (2) ◽

pp. 40

Author(s):

Hamsa D. Majeed

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Detection Algorithm ◽

Text Detection ◽

Anchor System ◽

Detection Model ◽

Characteristic Map ◽

Python Programming ◽

Language Detection ◽

F Measure

In this paper, a new text detection algorithm that accurately locates picture text with complex backgrounds in natural images is applied. The approach is based primarily on the region-based convolutional neural network anchor system, which takes into account the unique features of the text area, compares it to other object detection tasks, and turns the text area detection task into an object sensing task. Thus, the proposed text to be observed directly in the neural network’s convolutional characteristic map, and it can simultaneously predict the text/non-text score of the proposal and the coordinates of each proposal in the image. Then, we proposed an algorithm for the construction of the text line, to increase the text detection model accuracy and consistency. We found that our text detection operates accurately, even in multiple language detection functions. We also discovered that it meets the 2012 and 2014 International Conference on Document Analysis and Recognition thresholds of 0.86 F-measure and 0.78 F-measure, which clearly shows the consistency of our model. Our approach has been programmed and implemented using Python programming language 3.8.3 for Windows.

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Study on Intrusion detection model based on improved convolutional neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1865/4/042097 ◽

2021 ◽

Vol 1865 (4) ◽

pp. 042097

Author(s):

Kaiyan He

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Detection Model ◽

Model Based

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Enhanced Convolutional-Neural-Network Architecture for Crop Classification

Applied Sciences ◽

10.3390/app11094292 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4292

Author(s):

Mónica Y. Moreno-Revelo ◽

Lorena Guachi-Guachi ◽

Juan Bernardo Gómez-Mendoza ◽

Javier Revelo-Fuelagán ◽

Diego H. Peluffo-Ordóñez

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Classification Model ◽

Classification Error ◽

Small Scale ◽

Post Processing ◽

Average Accuracy ◽

Processing Step ◽

Crop Classification

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

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Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

Journal of Marine Science and Engineering ◽

10.3390/jmse9070755 ◽

2021 ◽

Vol 9 (7) ◽

pp. 755

Author(s):

Kangkang Jin ◽

Jian Xu ◽

Zichen Wang ◽

Can Lu ◽

Long Fan ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sea Ice ◽

Convolutional Neural Network ◽

Arctic Sea Ice ◽

The Arctic ◽

Small Scale ◽

Acoustic Tomography ◽

Arctic Sea ◽

Current Inversion

Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.

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Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

Electronics ◽

10.3390/electronics10222868 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2868

Author(s):

Wenxuan Zhao ◽

Yaqin Zhao ◽

Liqi Feng ◽

Jiaxi Tang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real World ◽

Large Scale ◽

Learning Strategy ◽

Contextual Information ◽

Small Scale ◽

Image Dehazing ◽

Atmospheric Scattering ◽

Real World Datasets

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.

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Lightweight Convolutional Neural Network Based Intrusion Detection System

Journal of Communications ◽

10.12720/jcm.15.11.808-817 ◽

2020 ◽

pp. 808-817

Author(s):

Vinh Pham ◽

◽

Eunil Seo ◽

Tai-Myoung Chung

Keyword(s):

Neural Network ◽

Machine Learning ◽

Intrusion Detection ◽

Convolutional Neural Network ◽

Network Traffic ◽

Detection System ◽

Image Data ◽

Training Data ◽

Deep Packet Inspection ◽

Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

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Performance Evaluation of Fine-tuned Faster R-CNN on specific MS COCO Objects

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2548-2555 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2548

Author(s):

Garima Devnani ◽

Ayush Jaiswal ◽

Roshni John ◽

Rajat Chaurasia ◽

Neha Tirpude

Keyword(s):

Neural Network ◽

Performance Evaluation ◽

Convolutional Neural Network ◽

Training Model ◽

Fine Tuning ◽

Training Process

<span lang="EN-US">Fine-tuning of a model is a method that is most often required to cater to the users’ explicit requirements. But the question remains whether the model is accurate enough to be used for a certain application. This paper strives to present the metrics used for performance evaluation of a Convolutional Neural Network (CNN) model. The evaluation is based on the training process which provides us with intermediate models after every 1000 iterations. While 1000 iterations are not substantial enough over the range of 490k iterations, the groups are sized with 100k iterations each. Now, the intention was to compare the recorded metrics to evaluate the model in terms of accuracy. The training model used the set of specific categories chosen from the Microsoft Common Objects in Context (MS COCO) dataset while allowing the users to use their externally available images to test the model’s accuracy. Our trained model ensured that all the objects are detected that are present in the image to depict the effect of precision.</span>

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