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Low-light Road Target Detection based on Lightweight Generative Adversarial Networks 3

Low-light Road Target Detection based on Lightweight Generat...

引用

3rd international conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Wei, Xiaowei Yu, Saisai He, Shuqing Linyi University School of Automation and Electrical Engineering Linyi China Linyi University School of Information Science and Engineering Linyi China

ISBN: (纸本)9798350316674

In road target detection scenarios, which are usually faced with diverse road conditions, target detection algorithms often fail to correctly identify the target when faced with low-brightness scenarios, such as at night, which can result in traffic accidents. However, the pictures enhanced by low-light image enhancement can clearly show the target features and effectively improve the accuracy of the target detection algorithm. In this paper, we use the EnlightenGAN model to enhance the low-light images selected from the BDD100K dataset, and at the same time, to improve the speed of image generation, we use a lightweight depth-separable convolutional module to replace the ordinary convolutional module, which reduces the computation volume to 1/8 of the original model and improves the inference speed by about 3 times. In YOLOv7 and NIQE evaluation indexes, the enhanced images generated by the improved lightweight model are similar to those generated by the original algorithm and substantially better than the traditional low-light image enhancement algorithm. © 2023 IEEE.

关键词： BDD100K dataset Deeply separable convolution EnlightenGAN YOLOv7

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An Overview on Designs and Applications of Context-Aware automation Systems 26

An Overview on Designs and Applications of Context-Aware Aut...

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26th international conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Sahlab, Nada Jazdi, Nasser Weyrich, Michael University of Stuttgart Institute of Industrial Automation and Software Engineering Pfaffenwaldring 47 Stuttgart70569 Germany

automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state-of-the-art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th international conference on Knowledge-Based and Intelligent Information & engineering Systems (KES 2022)

关键词： Cyber Physical System

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Automated Depression Detection Using CNN and Transformer-Based Pre-trained Language Models 8th

Automated Depression Detection Using CNN and Transformer-Bas...

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8th IFIP TC 12 international conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Ramalakshmi, N.R. Kumar, Meghna Ganesh Raghavi, S. St. Joseph’s College of Engineering OMR Tamil Nadu Chennai India

ISBN: (纸本)9783031736162

The research focuses on the automation of depression detection through the integration of Convolutional Neural Networks (CNNs) and Transformer-based Pre-Trained Language models. Traditionally, depression assessment relied on comprehensive clinical interviews where psychologists analyzed responses to assess an individual’s mental state. In this model, the replication of this process incorporates three modalities: word context, audio, and video. Through the fusion of these modalities, the prediction of an individual’s mental health status is achieved. The deep learning model encompasses three distinct data modalities, each representing different degrees of depression experienced by the subject. The model assigns appropriate weights to each modality, generating output based on their collective influence. This research contributes to advancing depression monitoring by employing cutting-edge deep learning techniques to analyze multi-modal data, leveraging independent learning synergies for enhanced accuracy. © IFIP international Federation for Information Processing 2025.

关键词： Convolutional neural networks

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Real-Time Bottle Classification Using MobileNetV2: An Evaluation of Beer, Plastic, Soda, Water, and Wine Bottle 2

Real-Time Bottle Classification Using MobileNetV2: An Evalua...

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2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Sharma, Jatin Adhav, Savita Anil Mahajan, Shubham Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Jspm Rajarshi Shahu College Of Engineering Dept-Information Technology Maharashtra Pune411033 India Deemed to Be University Department Of Computer Science Christ Delhi-NCR India

ISBN: (纸本)9798350364699

In several fields, especially retail, recycling, and trash management, object classification is important. This paper investigates five types of bottles: Beer Bottles, Plastic Bottles, Soda Bottles, Water Bottles, and Wine Bottles using a convolutional neural network (CNN). Based on a 25,000-image balanced dataset, the paper evaluates MobileNetV2's F1 score, accuracy, precision, and recall. Plastic bottles show almost perfect accuracy and recall of 99%, so the model attained an astounding general accuracy of 97%. Additionally showing great accuracy rates of 97% and 98%, respectively were water and beer bottles. The model clearly distinguishes between bottle types despite small misclassifications, including Soda Bottles being sometimes confused with Beer Bottles. Good model tuning with low over fitting is shown by the study of training and validation losses. The model's resilience even further is confirmed by the close alignment of the training and validation accuracy. The confusion matrix points up various misclassifications, which offer information for the next developments. This work highlights MobileNetV2's great fit for real-time bottle categorization uses, therefore providing major advantages for retail and recycling sectors of industry automation. Future research will concentrate on improving the model to solve found misclassification problems and raise its practical relevance. © 2024 IEEE.

关键词： Wine

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Research on Intelligent Inspection Robot Based on Neural Network and Visual SLAM Technology

Research on Intelligent Inspection Robot Based on Neural Net...

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2023 IEEE international conference on Electrical, automation and computer engineering, ICEACE 2023

作者： Wang, Chuyan He, Hongliang Guo, Chaoyang Huang, Silun Zhang, Jiwen Xu, Bo Li, Jiahui College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China College of Future Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798350309614

This article introduces the construction of a multi-foot drive pipeline inspection robot with multiple wheels, which can move forward and steer flexibly. The robot is divided into three main groups to perform different functions effectively: the front dredging group, the middle power group, and the tail function group. Each of these parts is specialized in a specific function. The adaptive dredging mechanism with reset lever and wheel-footed legs, and the compression lever structure can be adapted to different pipe diameters and enhance the stability of the robot's walking. The vision part contains an improved MobileNetV3 neural network model to perform target detection. The robot is capable of deploying the model and remotely monitoring the pipeline situation. This is made possible through the integration of OpenMV and ESP8266 modules. The OpenMV module enables the robot to capture high-definition images, providing a clear visual representation of the pipeline. These images are then transmitted through the ESP8266 module, allowing remote monitoring of the pipeline situation in real-time. It also expands the ORB-SLAM2 vision system to realize high-precision positioning through loopback detection, relocation and map reuse, which is applicable to the actual scenarios of pipeline mapping. The map building process is flexible and can eliminate redundant key frames and map points with large errors to improve robustness. The process includes data acquisition, visual odometer estimation and back-end optimization to achieve effective map building tasks. Overall tests show that the neural network model can reliably determine the debris in the pipeline, and the ORB-SLAM2 model can acquire the 3D model of the actual scene. Innovative solutions for pipeline detection are proposed, which can be capable of all kinds of innovative functions and tasks, such as image enhancement, intelligent analysis, and dredging processing of pipelines. © 2023 IEEE.

关键词： enhanced MobileNetV3 ORB-SLAM2 binocular vision pressure lever construction

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3D Modeling for a Flexible Detector in VR Radiography Simulator 7

3D Modeling for a Flexible Detector in VR Radiography Simula...

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7th international conference on Information Technologies in engineering Education, Inforino 2024

作者： Kuvshinnikov, Vladimir Kovshov, Evgeny Korchagin, Valery Institute of Construction Technology - Atomstroy Research Laboratory of Digital Computer Systems and Automation JSC 'Research and Development' Moscow Russia Dmitry Mendeleev University of Chemical Technology of Russia Disruptive Engineering School of Chemical Engineering and Machinery Moscow Russia

ISBN: (纸本)9798350371369

Digital simulators based on virtual reality expand the range of educational opportunities and improve the quality of learning in various areas of knowledge through deeper "immersion"in the learning process. Attention to the details of the modeled process plays an important role. One of the tasks of interactive simulators is to provide an opportunity to see the relationship between the user's actions and the obtained qualitative and quantitative parameters and quality attributes of the simulated process result. Considered the issue of curved shape detectors application relevance in the practice of penetrating radiation evaluation in general and in industrial radiography in particular. The prerequisites for the creation of flexible (bendable) form of digital radiographic detectors and the features of two generations of practically implemented devices are analyzed. Development of a flexible detector digital twin is considered, models and algorithms for its realization in a virtual reality environment are proposed, and the main results of computational and laboratory experiments as well as the obtained radiographic images are presented. The main task of virtual reality technologies within the qualified technical specialists training programs is the development of their professional skills and the formation of stable behavior patterns that ensure strict compliance with the rules and requirements of industrial safety, thereby ensuring the high quality of the end product. © 2024 IEEE.

关键词： Virtual reality

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VINS-Mask: A ROI-mask Feature Tracker for Monocular Visual-inertial System

VINS-Mask: A ROI-mask Feature Tracker for Monocular Visual-i...

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2022 international conference on automation, Robotics and computer engineering, ICARCE 2022

作者： Sun, Jiayuan Song, Fangwei Ji, Luping University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

ISBN: (纸本)9781665475488

Feature tracker is usually believed to be one of the most important components to the performance influence on a Visual-inertial System (VINS). This paper proposes the VINS-Mask scheme, a more robust feature tracker for monocular VINS through Region of Interest (ROI) masks. It could achieve real-time feature tracking with high accuracy and robustness. Firstly, we propose an edge mask to generate the edge-sensitive feature candidate regions from the incoming image frame. Next, we design an interest point sensitive SuperPoint mask with deep learning framework to obtain repeatable and reliable feature candidate regions. We also dynamically adjust the inflation radius by monitoring the initial status from VINS Initialization module to obtain more accurate ROI masks. Notably, compared with the best baseline approach (i.e., VINS-Mono), our VINS-Mask scheme achieves an average improvement accuracy of 0.068m on the dataset of EuRoc drone. After paper publication, our source codes will be available at https://***/sunjia-yuanro/***. © 2022 IEEE.

关键词： Deep learning

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Sucrose Crystal Classification and Quality Identification Using Screen Size and Yolov5

Sucrose Crystal Classification and Quality Identification Us...

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international conference on computer and automation engineering, ICCAE

作者： Aira Laurencia T. Navarro Chabelita Jaramilla Noel B. Linsangan School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

Sugarcane is a tropical grass that grows worldwide and is mainly utilized in sugar production. Even in the Philippines, it is one of the contributors for the country's revenue and can be found in most regions, spanning about 422,500 hectares. Sugar production in the Philippines is lower than in most producing countries, which presents a challenge for local sugarcane farmers. One of the reasons for this drop, according to the related studies, is that sugarcane farmers in provinces continue to employ conventional growing methods. Several studies have found that technology is being incorporated into agricultural equipment. utilizing this information, the researchers built a device with a Raspberry Pi and a web camera to classify and detect the quality of sucrose crystals with Yolov5.

关键词： YOLO automation Image processing Crystals Production Cameras Sugar

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Panchromatic loss based spectral-spatial dual injection network for extreme pansharpening 16

Panchromatic loss based spectral-spatial dual injection netw...

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16th international conference on Digital Image Processing, ICDIP 2024

作者： He, Lin Huang, Xiaolong Li, Jun School of Automation Science and Engineering South China University of Technology Guangzhou China School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

Pansharpening involves synthesizing high resolution hyperspectral images (HRHS) by combining low spatial resolution hyperspectral (LRHS) images and panchromatic (PAN) images. Existing methods perform inadequately in extreme panchromatic sharpening, often resulting in excessively blurred HRHS images. The main reasons are the combination of inputs and training loss functions overly simplistic, and the spatial details of upsampling LRHS are severely distorted, which leads to the weakening of the neural network's ability to utilize the spatial information of PAN images. To address this issue, we propose a spectral-spatial dual injection network (SSDINet) combined with panchromatic loss for extreme pansharpening. SSDINet alleviates the blur problem of HRHS images during extreme pansharpening by adding an additional pseudo hyperspectral (PHS) image input and combining it with upsampling LRHS images to form an additional spectral injection branch that is different from spatial injection. Additionally, during network training, use an extra panchromatic loss to alleviates the problem of incomplete utilization of PAN images. Panchromatic mapping is realized by neural network. Experimental results demonstrate the superior performance of our approach compared to representative methods. © 2024 SPIE.

关键词： Hyperspectral imaging

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Survival Modeling of Disease Consequences and Post-disease Syndromes

Survival Modeling of Disease Consequences and Post-disease...

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international conference on Medical Imaging and computer-Aided Diagnosis, MICAD 2023

作者： Haindl, Michal Institute of Information Theory and Automation Czech Academy of Sciences Pod Vodárenskou věží 4 Prague182 08 Czech Republic

ISBN: (纸本)9789819713349

We present a survival model for human maladies, which leave victims with permanent health damages requiring life-long medical observation and treatment. The model allows national health authorities to prepare sufficient medical specialists with adequate capacity in specialized clinics, vaccinations, spas, or rehabilitation facilities. We test the model on Czech Polio (Poliomyelitis Anterior Acuta) victims’ data. COVID-19 or Long Covid-19 and the treatment of their wide range of ongoing health problems, where these conditions can last weeks, months, or years, can benefit from Polio and COVID-19 RNA virus similarities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Viruses

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