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International Conference on Electrical, Communication, and Computer Engineering (ICECCE)

作者： Ranad Yousef Abed Aljammal Akshay Chavan Antonius Schmidt Dominik Aufderheide Industrial Measurement (iMe) Faculty of Electrical Engineering South Westphalia University of Applied Sciences Soest Germany

In energy-intensive industries, such as cement manufacturing, steal production, glass factories or in power plants classical fossil fuels, such as coal or gas, are replaced by more sustainable fuel sources derived from biomass and/or waste streams. However, these fuels tend to be quite volatile according to their main mechanical and chemical properties, such as particle size distribution, bulk density, etc. Therefore, real-time characterization methods need to be developed in order to allow an optimized utilization of such fuels in industry. This paper presents a visual approach for the self-acting estimation of the particle size distribution of a given fuel based on monocular image streams. The suggested methodology follows an image segmentation approach based on deep learning techniques. Due to the lack of ground truth data, an associated methodology for the automatic generation of a synthetic training data set is also presented. Different state-of-the-art segmentation models are implemented and trained on a training database for two different sustainable fuel sources. The performance is carefully validated by using different evaluation metrics. The implementation results showed that the deep learning approach produced more accurate segmented images compared to conventional image processing techniques.

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RAPID MAPPING OF LANDSLIDES TRIGGERED BY THE STORM ALEX, OCTOBER 2020

RAPID MAPPING OF LANDSLIDES TRIGGERED BY THE STORM ALEX, OCT...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Prakash, Nikhil Manconi, Andrea Swiss Fed Inst Technol Dept Earth Sci Engn Geol CH-8092 Zurich Switzerland

ISBN: (纸本)9781665403696

On 2nd and 3rd October 2020, Storm Alex hit northern Italy and southern France regions with 500 mm of rainfall in about 24 hours. This triggered devastating flash floods and landslides, causing severe damages and 15 fatalities. This study presents a landslide inventory map obtained by using a generalized deep-learning model, avoiding human interaction in the workflow by skipping the time-consuming training step. A total of 1,249 landslides have been mapped with this approach in minutes after a suitable post-event satellite image was available for processing. Our results show how deep-learning strategies applied to remote sensing data can help in the aftermath of catastrophic events for the rapid detection and mapping of landslide phenomena.

关键词： Landslide Rapid mapping Convolutional Neural Network (CNN) deep-learning Storm Alex

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Application of Recommendation Systems Based on deep learning 13th

Application of Recommendation Systems Based on Deep Learning

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13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021

作者： Haiming, Li Kaili, Wang Yunyun, Sun Xuefeng, Mou Shanghai University of Electric Power Shanghai201306 China

ISBN: (纸本)9789811616846

With the rapid development of mobile Internet and self-media, it is becoming more and more convenient for people to obtain information, and the problem of information overload has increasingly affected people’s sense of use. The emergence of recommendation systems can help solve the problem of information overload, but in the big data environment, the traditional recommendation system technology can no longer meet the needs of recommending more personalized, more real-time, and more accurate information to users. In recent years, deep learning has made breakthroughs in the fields of natural language understanding, speech recognition, and image processing. At the same time, deep learning has been integrated into the recommendation system, and satisfactory results have also been achieved. However, how to integrate massive multi-source heterogeneous data and build more accurate user and item models to improve the performance and user satisfaction of the recommendation system is still the main task of the recommendation system based on deep learning. This article reviews the research progress of recommendation systems based on deep learning in recent years and analyses the differences between recommendation systems based on deep learning and traditional recommendation systems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Hierarchical Residual Embedding Network for real-time De-focus Blur Detection 2

Hierarchical Residual Embedding Network for Real-Time De-foc...

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2020 2nd International Conference on Computer, Communications and Mechatronics Engineering, CCME 2020

作者： Zhang, Chunlei Nie, Jing Zheng, Xiao Electronic Engineering College Naval University of Engineering Wuhan430033 China Teaching and Research Support Center Naval University of Engineering Wuhan430033 China School of Computer Science National University of Defense Technology Changsha410073 China

Defocus blur detection, as an important pre-processing step of image processing, has attracted more and more attention. Albeit great success has been made, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales, missing boundary details and large computational burden. For handling these issues, we present a deep neural network which hierarchically embeds residual learning blocks for defocus blur detection. Based on the feature pyramid structure, we extract deep features with varying scales via utilizing a backbone fully convolutional network and generate a coarse score map by using the last layer of feature maps. Then we design a hierarchical residual embedding module to fuse different levels of features in a layer-wise manner. By embedding different layer-wise features in the top-down pathway, coarse-level semantic information from the deep layers can be seamlessly propagated to shallow layers, while fine details in the shallow layers can be used to refine the boundary between out-of-focus and in-focus regions. For each layer, a side output is generated by using a residual learning block. For capturing multi-scale information, the multiple side outputs of different layers are fed into a designed fusion block for yielding the final blur map result. Experimental results on two commonly used datasets show that our proposed network can more accurately locate the defocus blur regions with sharpened details being well preserved when compared to other previous state-of-the-arts. In addition, our approach is fast as well and can run at a speed of more than 25 FPS when processing an image with size 427 x 640. © Published under licence by IOP Publishing Ltd.

关键词： deep neural networks

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Weighted Feature Histogram of Multi-Scale Local Patch Using Multi-Bit Binary Descriptor for Face Recognition

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IEEE TRANSACTIONS ON image processing 2021年 30卷 3858-3871页

作者： Yang, Hua Gong, Chenting Huang, Kaiji Song, Kaiyou Yin, Zhouping Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Most face recognition methods employ single-bit binary descriptors for face representation. The information from these methods is lost in the process of quantization from real-valued descriptors to binary descriptors, which greatly limits their robustness for face recognition. In this study, we propose a novel weighted feature histogram (WFH) method of multi-scale local patches using multi-bit binary descriptors for face recognition. First, to obtain multi-scale information of the face image, the local patches are extracted using a multi-scale local patch generation (MSLPG) method. Second, with the goal of reducing the quantization information loss of binary descriptors, a novel multi-bit local binary descriptor learning (MBLBDL) method is proposed to extract multi-bit local binary descriptors (MBLBDs). In MBLBDL, a learned mapping matrix and novel multi-bit coding rules are employed to project pixel difference vectors (PDVs) into the MBLBDs in each local patch. Finally, a novel robust weight learning (RWL) method is proposed to learn a set of robust weights for each patch to integrate the MBLBDs into the final face representation. In RWL, a codebook is first constructed by clustering MBLBDs on each local patch to extract a feature histogram. Then, considering that different parts of the face have different degrees of robustness to local changes, a set of weights is learned to concatenate the feature histograms of all local patches into the final representation of a face image. In addition, to further improve the performance for heterogeneous face recognition, a coupled WFH (C-WFH) method is proposed. C-WFH maintains the similarity of the corresponding MBLBDs and feature histograms for a pair of heterogeneous face images by means of a novel coupled feature learning (CFL) method to reduce the modality gap. A series of experiments are conducted on widely used face datasets to analyze the performance of WFH and C-WFH. Extensive experimental results show that WFH and C-

关键词： Face recognition Feature extraction Histograms Robustness Quantization (signal) Task analysis real-time systems Face recognition feature learning multi-bit binary descriptor heterogeneous face matching

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Detection and localization of rebar in concrete by deep learning using ground penetrating radar

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AUTOMATION IN CONSTRUCTION 2020年 118卷 103279-103279页

作者： Liu, Hai Lin, Chunxu Cui, Jie Fan, Lisheng Xie, Xiongyao Spencer, Billie F. Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China Tongji Univ Dept Geotech Engn Shanghai 200092 Peoples R China Univ Illinois Dept Civil & Environm Engn Champaign IL 61801 USA

Ground penetrating radar (GPR) has been widely used for detection and localization of reinforced steel bar (rebar) in concrete in a non-destructive way. However, manual interpretation of a large number of GPR images is time-consuming, and the results highly depend on practitioner experience and the available priori information. This paper proposes an automatic detection and localization method using deep learning and migration. Firstly, a Single Shot Multibox Detector (SSD) model is established to identify regions of interest containing hyperbolas in a GPR image. This deep learning model is trained using a real GPR dataset, which contains 13,026 rebar targets in 3992 images, collected on residential buildings under construction. Secondly, each target region is migrated and transformed into a binary image to locate the rebar. After the binarization, the apex of the focused cluster is obtained and used to estimate both the horizontal position and the depth of the rebar. The testing results show that the detection accuracy of the proposed artificial intelligence method is 90.9%. The computation time needed for processing a GPR image with a size of 300 x 300 pixels is only 0.47 s. The depth estimation error in a laboratory experiment is < 1.5 mm (5%), and the lateral position error is < 0.7 cm. Therefore, it is concluded that the proposed method can automatically detect the rebar from GPR images in real time when a handheld GPR system is operated at a walking speed and the depth estimation accuracy is acceptable in practice.

关键词： Ground penetrating radar (GPR) Rebar detection deep learning Migration Cover thickness

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Study of VGG-19 Depth in Transfer learning for COVID-19 X-Ray image Classification 11th

Study of VGG-19 Depth in Transfer Learning for COVID-19 X-Ra...

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11th International Conference on Robotics, Vision, Signal processing and Power Applications, RoViSP 2021

作者： Hamad, Qusay Shihab Samma, Hussein Suandi, Shahrel Azmin Saleh, Junita Mohamad School of Electrical and Electronic Engineering Engineering Campus Universiti Sains Malaysia Penang Nibong Tebal14300 Malaysia Johor Johor Bahru81310 Malaysia Baghdad Iraq

ISBN: (纸本)9789811681288

Modern-era largely depends on deep learning (DL) in a lot of applications. Medical images Diagnosis is one of the important fields nowadays because it is related to human life. But this DL requires large datasets as well as powerful computing resources. At the beginning of 2020, the world faced a new pandemic called COVID-19. Since it is new, shortage of reliable datasets of a running pandemic is a common phenomenon. One of the best solutions to mitigate this shortage is taking advantage of deep Transfer learning (DTL). DTL would be useful because it learns from one task and could work on another task with a smaller amount of dataset. This paper aims to examine the application of the transferred VGG-19 to solve the problem of COVID-19 detection from a chest x-ray. Different scenarios of the VGG-19 have been examined, including shallow model, medium model, and deep model. The main advantages of this work are two folds: COVID-19 patient can be detected with a small number of data sets, and the complexity of VGG-19 can be reduced by reducing the number of layers, which consequently reduces the training time. To assess the performance of these architectures, 2159 chest x-ray images were employed. Reported results indicated that the best recognition rate was achieved from a shallow model with 95% accuracy while the medium model and deep model obtained 94% and 75%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Motion target detection and recognition based on YOLOv4 algorithm 3

Motion target detection and recognition based on YOLOv4 algo...

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2021 3rd International Conference on Artificial Intelligence and Computer Science, AICS 2021

作者： Yu, Hailan Chen, Weili School of Information Technology and Engineering Guangzhou College of Commerce Guangdong Guangzhou China

With the application of artificial intelligence more and more widely, the target detection of artificial intelligence "eyes" is becoming more and more important, which can give the machine the ability to detect and recognize image content in the environment. In this paper, YOLOv4 deep learning motion target detection algorithm is used to achieve the positioning and recognition of motion targets, to identify and mark the location and type of objects contained in the image, to achieve the detection of pictures, videos and camera real-time image detection, so that the machine has the most basic visual ability. Using YOLOv4 algorithm, the speed requirement of real-time detection can be realized with certain precision, and the model mAP value reached 69.48%. © Journal of Physics: Conference Series 2021.

关键词： deep learning

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Supervised GAN Watermarking for Intellectual Property Protection

Supervised GAN Watermarking for Intellectual Property Protec...

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IEEE International Workshop on Information Forensics and Security (WIFS)

作者： Fei, Jianwei Xia, Zhihua Tondi, Benedetta Barni, Mauro Nanjing Univ Informat Sci & Technol Nanjing Peoples R China Jinan Univ Guangzhou Peoples R China Univ Siena Siena Italy

ISBN: (数字)9798350309676

ISBN: (纸本)9798350309676

We propose a watermarking method for protecting the Intellectual Property (IP) of Generative Adversarial Networks (GANs). The aim is to watermark the GAN model so that any image generated by the GAN contains an invisible watermark (signature), whose presence inside the image can be checked at a later stage for ownership verification. To achieve this goal, a pre-trained CNN watermarking decoding block is inserted at the output of the generator. The generator loss is then modified by including a watermark loss term, to ensure that the prescribed watermark can be extracted from the generated images. The watermark is embedded via fine-tuning, with reduced time complexity. Results show that our method can effectively embed an invisible watermark inside the generated images. Moreover, our method is a general one and can work with different GAN architectures, different tasks, and different resolutions of the output image. We also demonstrate the good robustness performance of the embedded watermark against several post-processing, among them, JPEG compression, noise addition, blurring, and color transformations.

关键词： Intellectual Property Protection Generative Adversarial Networks DNN Watermarking Security of deep learning

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Multisource Mobile Transfer learning Algorithm Based on Dynamic Model Compression

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SECURITY AND COMMUNICATION NETWORKS 2022年第1期2022卷

作者： Gao, Peng Li, Jingmei Ding, Changhong Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China Heilongjiang Broadcasting Stn Converged Media Technol Dept Harbin Peoples R China Heilongjiang Univ Chinese Med Harbin Peoples R China

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

关键词： image classification

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