scholarly journals Evaluation of Deep Learning-Based Segmentation Methods for Industrial Burner Flames

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1716
Author(s):  
Julius Großkopf ◽  
Jörg Matthes ◽  
Markus Vogelbacher ◽  
Patrick Waibel
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Processing System ◽  
Image Data ◽  
Waste Incineration ◽  
Industrial Environment ◽  
Camera Systems ◽  
Robust Segmentation ◽  
Combustion Processes ◽  
Instance Segmentation

The energetic usage of fuels from renewable sources or waste material is associated with controlled combustion processes with industrial burner equipment. For the observation of such processes, camera systems are increasingly being used. With additional completion by an appropriate image processing system, camera observation of controlled combustion can be used for closed-loop process control giving leverage for optimization and more efficient usage of fuels. A key element of a camera-based control system is the robust segmentation of each burners flame. However, flame instance segmentation in an industrial environment imposes specific problems for image processing, such as overlapping flames, blurry object borders, occlusion, and irregular image content. In this research, we investigate the capability of a deep learning approach for the instance segmentation of industrial burner flames based on example image data from a special waste incineration plant. We evaluate the segmentation quality and robustness in challenging situations with several convolutional neural networks and demonstrate that a deep learning-based approach is capable of producing satisfying results for instance segmentation in an industrial environment.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

Image intelligent automatic processing analysis based on artificial intelligence vision generator

2020 ◽  
pp. 1-10
Author(s):  
Ruijuan Wang ◽  
Wei Zhuo
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Deep Learning ◽  
Human Error ◽  
Processing System ◽  
Image Data ◽  
Visual Image ◽  
Automated Analysis ◽  
Learning Method ◽  
Deep Learning Model

The image intelligent processing analysis technology uses a computer to imitate and execute some intellectual functions of the human brain, and realizes an image processing system with artificial intelligence, that is, an image processing analysis technology is an understanding of an image. The degree of intelligent automated analysis and processing is low, many operations need to be done manually, causing human error, inaccurate detection, and time-consuming and laborious. Deep learning method can extract features step by step in the original image from the bottom to the top. Therefore, based on feature analysis technology, this paper uses the deep learning method to intelligently and automatically analyse the visual image. This method only needs to send the image into the system, and then the manual analysis is not needed, and the analysis result of the final image can be obtained. The process is completely intelligent and automatically processed. First, improve the deep learning model and use massive image data to choose and optimize parameters. Results indicate that our method not only automatically derives the semantic information of the image, but also accurately understands the image accurately and improve the work efficiency.

scholarly journals Design and implementation of remote image processing system for space launch site

2021 ◽  
Vol 233 ◽  
pp. 04026
Author(s):  
Ma Li ◽  
Wang Bai Yan ◽  
Liu Tao ◽  
WangYu Chao ◽  
Xiang Yu ◽  
...  
Keyword(s):  
Image Processing ◽  
Auxiliary Information ◽  
Processing System ◽  
Image Data ◽  
Image Transmission ◽  
Engineering Practice ◽  
Image Processing System ◽  
Rocket Flight ◽  
Launch Site ◽  
Space Launch

Telemetry image has the characteristics of intuitive image in the process of rocket flight. Through real-time acquisition of rocket flight video image, it can provide the working status of key nodes in the process of rocket flight, and provide intuitive decision-marking auxiliary information for commanders. This paper analyzes the design content of the image processing system of the space launch site from the aspects of image transmission mechanism, information flow, image data processing and image decoding, so as to provide technical basis for the image receiving, transmission and decoding process in the engineering practice of the image processing system.

Design and Realization of Double-Buses Modularized Structure Image Processing System

2011 ◽  
Vol 179-180 ◽  
pp. 257-263
Author(s):  
Biao Zhang ◽  
Yue Huan Wang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing System ◽  
Image Data ◽  
Parallel Structure ◽  
Image Processing System ◽  
Real Time Image Processing ◽  
Real Time Image ◽  
Data Bus ◽  
Time Image

It is double-buses modularized structure with the combination of system control bus and high speed image data bus which is put forward in this paper. Moreover, the management and distribution of image data bus and the design of system reset procedure are elaborated through which a kind of practical real-time image processing system with the strongest adaptability and capability for structure programming and system expansion. The computing capability in infrared test of small target is greatly improved which is verified in tri DSP model system. According to complex image processing task, through the adjustment of parallel structure of image processing algorithm, the higher parallel efficiency can be realized. So to say, the system structure has a great adjustment to algorithm parallel structure and can be successfully used as a platform for universal real-time image processing.

scholarly journals Embedded Real-Time Image Processing System Based on DM6446+FPGA Architecture

2012 ◽  
Vol 6-7 ◽  
pp. 542-546
Author(s):  
Bao Feng Zhang ◽  
Yi Yang ◽  
Jun Chao Zhu ◽  
Cui Li
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing System ◽  
Image Data ◽  
Image Processing System ◽  
Real Time Image Processing ◽  
Fpga Architecture ◽  
Real Time Image ◽  
Output Field ◽  
Time Image

To solve the traditional image processing system problem such as large in size, high power consumption and poor real-time, an embedded real-time image processing system is designed based on TMS320DM6446+FPGA architecture. DM6446 as the core of the system is responsible for the scheduling, image processing algorithms, image output; field programmable gate array (FPGA) is responsible for capturing real-time image data, image preprocessing. The paper describes the principle of the real-time image processing system. The experiment proved that the system can achieve real-time acquisition, processing and output of image data in 20 frames per second.

scholarly journals Combining Low-Level Image Features with Features from A Simple Convolutional Neural Network

2019 ◽  
pp. 136-139
Author(s):  
Ozge Oztimur Karadag ◽  
Ozlem Erdas
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Data ◽  
Image Features ◽  
Data Set ◽  
Low Level ◽  
Learning Techniques ◽  
Processing Techniques ◽  
Layered Architectures ◽  
Traditional Image

In the traditional image processing approaches, first low-level image features are extracted and then they are sent to a classifier or a recognizer for further processing. While the traditional image processing techniques employ this step-by-step approach, majority of the recent studies prefer layered architectures which both extract features and do the classification or recognition tasks. These architectures are referred as deep learning techniques and they are applicable if sufficient amount of labeled data is available and the minimum system requirements are met. Nevertheless, most of the time either the data is insufficient or the system sources are not enough. In this study, we experimented how it is still possible to obtain an effective visual representation by combining low-level visual features with features from a simple deep learning model. As a result, combinational features gave rise to 0.80 accuracy on the image data set while the performance of low-level features and deep learning features were 0.70 and 0.74 respectively.

scholarly journals InstantDL - An easy-to-use deep learning pipeline for image segmentation and classification

2020 ◽  
Author(s):  
Dominik Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Biomedical Image ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

AbstractMotivationDeep learning contributes to uncovering and understanding molecular and cellular processes with highly performant image computing algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate, consistent and fast data processing. However, published algorithms mostly solve only one specific problem and they often require expert skills and a considerable computer science and machine learning background for application.ResultsWe have thus developed a deep learning pipeline called InstantDL for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables experts and non-experts to apply state-of-the-art deep learning algorithms to biomedical image data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows to assess the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible.Availability and ImplementationInstantDL is available under the terms of MIT licence. It can be found on GitHub: https://github.com/marrlab/[email protected]

scholarly journals Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

2018 ◽  
Author(s):  
Yuta Tokuoka ◽  
Takahiro G Yamada ◽  
Noriko F Hiroi ◽  
Tetsuya J Kobayashi ◽  
Kazuo Yamagata ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Critical Issue ◽  
Segmentation Algorithm ◽  
Embryonic Cells ◽  
Mouse Development ◽  
Instance Segmentation

AbstractIn embryology, image processing methods such as segmentation are applied to acquiring quantitative criteria from time-series three-dimensional microscopic images. When used to segment cells or intracellular organelles, several current deep learning techniques outperform traditional image processing algorithms. However, segmentation algorithms still have unsolved problems, especially in bioimage processing. The most critical issue is that the existing deep learning-based algorithms for bioimages can perform only semantic segmentation, which distinguishes whether a pixel is within an object (for example, nucleus) or not. In this study, we implemented a novel segmentation algorithm, based on deep learning, which segments each nucleus and adds different labels to the detected objects. This segmentation algorithm is called instance segmentation. Our instance segmentation algorithm, implemented as a neural network, which we named QCA Net, substantially outperformed 3D U-Net, which is the best semantic segmentation algorithm that uses deep learning. Using QCA Net, we quantified the nuclear number, volume, surface area, and center of gravity coordinates during the development of mouse embryos. In particular, QCA Net distinguished nuclei of embryonic cells from those of polar bodies formed in meiosis. We consider that QCA Net can greatly contribute to bioimage segmentation in embryology by generating quantitative criteria from segmented images.

scholarly journals Instance Segmentation Method of User Interface Component of Games

2020 ◽  
Vol 10 (18) ◽  
pp. 6502
Author(s):  
Shinjin Kang ◽  
Jong-in Choi
Keyword(s):  
Deep Learning ◽  
Semantic Information ◽  
Data Augmentation ◽  
Image Data ◽  
Learning Networks ◽  
Segmentation Method ◽  
Learning Network ◽  
Additional Input ◽  
Deep Learning Network ◽  
Instance Segmentation

On the game screen, the UI interface provides key information for game play. A vision deep learning network exploits pure pixel information in the screen. Apart from this, if we separately extract the information provided by the UI interface and use it as an additional input value, we can enhance the learning efficiency of deep learning networks. To this end, by effectively segmenting UI interface components such as buttons, image icons, and gauge bars on the game screen, we should be able to separately analyze only the relevant images. In this paper, we propose a methodology that segments UI components in a game by using synthetic game images created on a game engine. We developed a tool that approximately detected the UI areas of an image in games on the game screen and generated a large amount of synthetic labeling data through this. By training this data on a Pix2Pix, we applied UI segmentation. The network trained in this way can segment the UI areas of the target game regardless of the position of the corresponding UI components. Our methodology can help analyze the game screen without applying data augmentation to the game screen. It can also help vision researchers who should extract semantic information from game image data.

Research of Embedded Image Processing Based on ARM11 and SQLite

2013 ◽  
Vol 373-375 ◽  
pp. 1603-1606
Author(s):  
Chen Chen Liu
Keyword(s):  
Image Processing ◽  
Embedded Systems ◽  
Embedded System ◽  
Research Program ◽  
Processing System ◽  
Image Data ◽  
Store Image ◽  
Efficient Management ◽  
The Embedded System ◽  
Embedded Image

According to the fact that the embedded system is not efficient enough to access and manipulate image data, this paper put forward a research program of image JPEG compression algorithm and being stored in a combination based on the ARM11 and SQLite embedded database image processing system. Comparative Researches on the system without data prove that the program can make the embedded systems more reasonable to store image data and realize the localized efficient management of the image data, which has certain practical value.

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