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29th European Signal processing Conference (EUSIPCO)

作者： Yu, Yide Shandiz, Amin Honarmandi Toth, Laszlo Univ Szeged Inst Informat Szeged Hungary

ISBN: (纸本)9789082797060

Several approaches exist for the recording of articulatory movements, such as eletromagnetic and permanent magnetic articulagraphy, ultrasound tongue imaging and surface electromyography. Although magnetic resonance imaging (MRI) is more costly than the above approaches, the recent developments in this area now allow the recording of real-time MRI videos of the articulators with an acceptable resolution. Here, we experiment with the reconstruction of the speech signal from a real-time MRI recording using deep neural networks. Instead of estimating speech directly, our networks are trained to output a spectral vector, from which we reconstruct the speech signal using the WaveGlow neural vocoder. We compare the performance of three deep neural architectures for the estimation task, combining convolutional (CNN) and recurrence-based (LSTM) neural layers. Besides the mean absolute error (MAE) of our networks, we also evaluate our models by comparing the speech signals obtained using several objective speech quality metrics like the mean cepstral distortion (MCD), Short-time Objective Intelligibility (STOI), Perceptual Evaluation of Speech Quality (PESQ) and Signal-to-Distortion Ratio (SDR). The results indicate that our approach can successfully reconstruct the gross spectral shape, but more improvements are needed to reproduce the fine spectral details.

关键词： real-time MRI articulatory-to-acoustic mapping deep learning

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Detection of Unathorized Vechile in BRT Lane

Detection of Unathorized Vechile in BRT Lane

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IEEE Punecon

作者： Pallavi Nikumbh Pankaj More Harsh Vahal Pratham Mali Prathmesh Mohite Department of Computer Science and Engineering (AI&ML) Pimpri Chinchwad College of Engineering Pune India Department of Computer Science and Engineering Pimpri Chinchwad College of Engineering Pune India

ISBN: (数字)9798331527822

ISBN: (纸本)9798331527839

The increasing number of unauthorized vehicles in the Bus Rapid Transit (BRT) lanes creates a significant challenge to urban transportation. Due to this efficiency and quality of safety decreases. Our project aims to develop an automated system that detects unauthorized vehicles that come into BRT lanes using deep learning techniques like YOLOv8. The system employs combination video surveillance, image processing, and deep learning models to accurately identify and track vehicles that violate the BRT regulations. The proposed solution enhances traffic management and reduces the incidence of unauthorized lane usage by integrating the real- time data processing and alert mechanisms. The system employs a dataset from Roboflow, extended with a self-made dataset containing images of various vehicles in BRT lanes. We utilize YOLOv8 for object detection to accurately identify and classify vehicles. Also, Easy-OCR is used to recognize text from license plates for validation and verification against authorized vehicle list. Our preliminary results demonstrate that the system achieves 80% accuracy in detecting unauthorized vehicles. Our solution is urban traffic management by reducing illegal lane usage and ensuring easier public transport operations.

关键词： deep learning Text recognition Urban areas Transportation Object detection Video surveillance Regulation Safety Object recognition License plate recognition

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real-time running detection system for UAV imagery based on optical flow and deep convolutional networks

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第5期14卷 278-287页

作者： Wu, Qingtian Zhou, Yimin Wu, Xinyu Liang, Guoyuan Ou, Yongsheng Sun, Tianfu Chinese Acad Sci Shenzhen InstAdv Technol Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synerg Syst Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Robot & Intelligent Syst Shenzhen Peoples R China

A fast-running human detection system for the unmanned aerial vehicle (UAV) based on optical flow and deep convolution networks is proposed in this study. In the system, running humans can be detected in real-time at the speed of 15 frames per second (fps) with an 81.1% detection accuracy. To fast locate the candidate targets, optical flow representing the motion information is calculated with every two successive frames. A series of prior-processing operations, including spatial average filtering, morphological expansion and outer contour extraction, are performed to extract the regions of interest. A classification model based on small-kernel convolution networks is proposed to achieve the accurate recognition of the running people in various backgrounds. In the model, small convolutional filters are adopted to accelerate the speed of the data representation. Moreover, a total of 60,000 samples are collected to enhance the robustness of the model to adapt to the complex outdoor UAV scenes. The proposed method is compared with other deep learning frameworks for object detection. Field experiments on UAV videos are performed to verify that the proposed system can effectively detect the running people targets in real-time.

关键词： autonomous aerial vehicles feature extraction image recognition object detection image classification mobile robots image sequences learning (artificial intelligence) remotely operated vehicles optical flow deep convolutional networks human detection system unmanned aerial vehicle deep convolution networks 81 1% detection accuracy successive frames prior-processing operations spatial average filtering outer contour extraction small-kernel convolution networks running people convolutional filters complex outdoor UAV scenes deep learning frameworks object detection UAV videos time running detection system UAV imagery

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A new weakly supervised learning approach for real-time iron ore feed load estimation

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷

作者： Guo, Li Peng, Yonghong Qin, Rui Liu, Bingyu Manchester Metropolitan Univ Dept Comp & Math Manchester England Ansteel Min Corp Anshan Liaoning Peoples R China

Iron ore feed-load control is one of the most critical settings in a mineral grinding process. It has direct impact on the quality of final iron products. The setting of the feed load heavily replies the characteristics of the ore pellets. However, such characteristics are challenging to acquire in many production environments, requiring speical equipments and complicated modelling process with a high cost. To provide an low-cost and easier-to-implement solution, in this paper, we present our work on using deep learning models for direct ore feed load estimation from ore pellet images. To address the challenges caused by the large size of ore images and the shortage of accurately annotated data, we proposed to use a weakly supervised learning apporach with a two-stage model training algorithm and two neural network architectures developed. The experiment results show competitive model performance, and the trained models can be used for real-time feed load estimation for grind process optimisation.

关键词： Mineral processing deep learning Iron ore image processing Weakly supervised learning

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A Benchmark of deep learning Models for Multi-leaf Diseases for Edge Devices

A Benchmark of Deep Learning Models for Multi-leaf Diseases ...

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International Conference on Advanced Technologies for Communications (ATC)

作者： Pham Tuan Anh Hoang Trong Minh Duc Posts & Telecommun Inst Technol Hanoi Vietnam Hanoi Univ Sci & Technol SET Hanoi Vietnam

ISBN: (纸本)9781665433792

Every season, leaf diseases are one of the main causes affecting the production of many crops, which cause enormous damage to farmers. To minimize the loss, deep learning techniques are utilized to detect leaf infection and have wildly outperformed the traditional method of manual detection. However, deploying such models is a challenge since devices in the field normally have limited resources and low computational power while large datasets have to be used. Therefore, in this paper, we benchmark the most popular deep learning models for multi-leaf disease detection to gauge which model is the most suitable for real deployment. Using a real-world large-scale dataset from PlantVillage and a Raspberry Pi 3, we found that MobileNet V3 provides a reliable accuracy of 96.58%, small Inference/Initialization time of 127 ms and 11 ms respectively, requires only 7.4 MB of memory in total, and hence the most appropriate choice for a real farm.

关键词： deep learning edge computing precision agriculture multi-leaf disease image processing

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Successive Graph Convolutional Network for image De-raining

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2021年第5期129卷 1691-1711页

作者： Fu, Xueyang Qi, Qi Zha, Zheng-Jun Ding, Xinghao Wu, Feng Paisley, John Univ Sci & Technol China Sch Informat Sci & Technol Hefei Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Columbia Univ Dept Elect Engn New York NY 10027 USA Columbia Univ Data Sci Inst New York NY USA

deep convolutional neural networks (CNNs) have shown their advantages in the single image de-raining task. However, most existing CNNs-based methods utilize only local spatial information without considering long-range contextual information. In this paper, we propose a graph convolutional networks (GCNs)-based model to solve the above problem. We specifically design two graphs to extract representations from new dimensions. The first graph models the global spatial relationship between pixels in the feature, while the second graph models the interrelationship across the channels. By integrating conventional CNNs and our GCNs into a single framework, the proposed method is able to explore comprehensive feature representations from three aspects, i.e., local spatial patterns, global spatial coherence and channel correlation. To better exploit the explored rich feature representations, we further introduce a simple yet effective recurrent operations to perform the de-raining process in a successive manner. Benefiting from the rich information exploration and exploitation, our method achieves state-of-the-art results on both synthetic and real-world data sets.

关键词： image de-raining Graph convolutional networks deep learning image processing

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Perlin Random Erasing for Data Augmentation 29

Perlin Random Erasing for Data Augmentation

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29th IEEE Conference on Signal processing and Communications Applications (SIU)

作者： Saran, Murat Nar, Fatih Saran, Ayse Nurdan Cankaya Univ Dept Comp Engn Ankara Turkey Ankara Yildirim Beyazit Univ Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781665436496

In the last decade, deep learning is applied in a wide range of problems with tremendous success. Large data, increased computational resources, and theoretical improvements are main reasons for this success. As the dataset grows, the real-world is better represented, allows developing a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, or sometimes even challenging. Therefore, researchers proposed data augmentation methods to increase dataset size by creating variations of the existing data. This study proposes an extension to Random Erasing data augmentation method by introducing smoothness. The proposed method provides better performance compared to Random Erasing data augmentation method, which is shown using a transfer learning scenario on the UC Merced Land-use image dataset.

关键词： data augmentation random erasing deep learning

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deep learning-Based Smart Parking Management System and Business Model 1

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5th International Conference on Computer Vision and image processing, CVIP 2020

作者： Kher, Yatharth Saxena, Aditya Tamizharasan, P.S. Joshi, Amit D. Pilani Dubai Campus Dubai United Arab Emirates Department of Computer Engineering and IT College of Engineering Pune Pune MS India

ISBN: (数字)9789811611032

ISBN: (纸本)9789811611025

In this fast-developing world, the increase in the number of vehicles demands a smart parking system in smart cities. The issue of spending a lot of time finding parking slots needs to be addressed. The increase of smartphones provides the space to develop smart applications enabled with AI and deep learning. This paper proposes an AI-based smart parking management system and a business model to provide a solution for both user and the owner of the parking space. Owners of the parking slots can opt for fixed or variable timeslots to make use of their parking spaces. Registered users can check the availability of the parking spaces at the destination in real-time and details of the users such as the time and vehicle details can be detected and updated automatically. Billing for the parking space usage will also be done automatically as per the regulated guidelines. Raspberry Pi and deep learning tools are used for the implementation. The proposed system is cost-effective and reduces time and energy. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Android (operating system)

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A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery

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DENTOMAXILLOFACIAL RADIOLOGY 2022年第7期51卷 no-no页

作者： Minnema, Jordi Ernst, Anne van Eijnatten, Maureen Pauwels, Ruben Forouzanfar, Tymour Batenburg, Kees Joost Wolff, Jan Vrije Univ Amsterdam Amsterdam UMC Dept Oral & Maxillofacial Surg Pathol 3D InnovationlabAmsterdam Movement Sci Amsterdam Netherlands Vrije Univ Amsterdam Acad Ctr Dent Amsterdam ACTA 3D Innovationlab Amsterdam Movement Sci Amsterdam Netherlands Univ Hosp Hamburg Eppendorf Inst Med Syst Biol Hamburg Germany Ctr Wiskunde & Informat CWI Computat Imaging Grp Amsterdam Netherlands Eindhoven Univ Technol Dept Biomed Engn Med Image Anal Grp Eindhoven Netherlands Aarhus Univ Aarhus Inst Adv Studies Aarhus Denmark Leiden Univ Leiden Inst Adv Comp Sci LIACS Leiden Netherlands Aarhus Univ Dept Dent & Oral Hlth Vennelyst Blvd Aarhus Denmark

Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxil-lofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and prom-ising research avenues are *** Radiology (2022) 51, 20210437. doi: 10.1259/dmfr.20210437

关键词： computer-assisted surgery neural networks CT image reconstruction bone segmentation surgical planning

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

6th International Conference on Emerging Research in Computi...

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6th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2020

ISBN: (纸本)9789811613418

The proceedings contain 44 papers. The special focus in this conference is on Emerging Research in Computing, Information, Communication and Applications. The topics include: Decision Tree Based Crop Yield Prediction Using Agro-climatic Parameters;a deep learning Approach for Speed Bump and Pothole Detection Using Sensor Data;driver Activity Monitoring Using MobileNets;Smart College Camera Security System Using IOT;IoT-CBSE: A Search Engine for Semantic Internet of Things;Static and Dynamic Human Activity Detection Using Multi CNN-ELM Approach;water Table Analysis Using Machine learning;real-time image Deblurring and Super Resolution Using Convolutional Neural Networks;proficient Detection of Flash Attacks Using a Predictive Strategy;automatic Gate Control System;An Effective PUF Based Lightweight Authentication and Key Sharing Scheme for IoT Devices;CYPUR-NN: Crop Yield Prediction Using Regression and Neural Networks;TORA: Text Summarization Using Optical Character Recognition and Attention Neural Networks;development of Security Performance and Comparative Analyses Process for Big Data in Cloud;design of a Secure Blockchain Based Privacy Preserving Electronic Voting System;sentiment Analysis to Detect Depression in Social Media Users: Overview and Proposed Methodology;Aquatic Debris Detection System: image processing—IOT Integration Perspective;ioT-Based Water Quality Analysis and Purification System;image Captioning for the Visually Impaired;dynamic Resource Allocation for Virtual Machines in Cloud Data Center;on the Maximum N-degree Energy of Graphs;Navigation Assistance and Collision Avoidance for the Visually Impaired "NACVI";Predicting the Rate of Transmission of Viral Diseases Using GARCH;foggy Security;flood Monitoring and Alerting System for Low Lying Urban Areas;health Assistant Bot.

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