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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Deep Learning Based Visual SLAM Algorithm for Indoor Dynamic Scene 3

Deep Learning Based Visual SLAM Algorithm for Indoor Dynamic...

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3rd international conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Hu, Jiwei Zhang, Qianpeng Jin, Qiwen School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359961

Simultaneous localization and mapping algorithms find extensive applications in the field of robotics, holding a pivotal position. However, in the presence of dynamic targets, the positional accuracy of SLAM systems tends to diminish. In order to handle this issue, a dynamic visual SLAM algorithm based on deep learning is proposed in this paper. By incorporating the object detection network, it assists the SLAM system in distinguishing dynamic targets, thereby enhancing the reliability in dynamic settings. The experiment results show that, in dynamic environments, this algorithm achieves an average enhancement of 90% in positional accuracy when compared to ORB-SLAM2. © 2023 IEEE.

关键词： dynamic feature point elimination object detection SLAM

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Cloud based Landslide detection and Alerting Nearby People by using IoT technology 3

Cloud based Landslide Detection and Alerting Nearby People b...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Gopi, B. Premalatha, J. Kalaivani, R. Ravikumar, D. Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Electronics and Communications Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Biotechnology Mit College of Arts and Science for Women Trichy Tamil Nadu Musiri India Department of Electronics and Communications Engineering Kings Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Landslides are one of the most devastating natural disasters that can strike a region. They are caused by the movement of large amounts of earth, rock, and other material down a slope. Landslides are caused by rain, snow, and other precipitation that causes soil to become saturated and unable to support the loads that are placed on it. Landslides can also be triggered by earthquakes or human activities such as mining, construction, and quarrying. Internally generated Internet of Things network and system acquisition generation Landslides were detected using humidity sensors, accelerometers, and vibration sensors, as well as GPS and a siren to inform people. You may charge a little price for this sensor, and if the fee surpasses the basic cost, you can approximately watch people in preparation of an imminent landslide, and big losses are avoided. The microcontroller collects and updates statistics from websites using the MQTT protocol. These telemetry flights can assist folks become aware of an oncoming crisis and have a better understanding of the situation. © 2023 IEEE.

关键词： Internet of things

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MDFN:Multi-scale Dense Fusion Network for RGB-D Salient Object detection 3

MDFN:Multi-scale Dense Fusion Network for RGB-D Salient Obje...

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3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Wen, Xinyu Wang, Feng Feng, Zhengyong Lin, Jun Shi, Chengkun China West Normal University College of Electronic and Information Engineering Nanchong637000 China Weinan Normal University College of Physics and Electrical Engineering Weinan714009 China

ISBN: (纸本)9781665490788

In RGB-D salient object detection, the low quality of the salient maps produced after detection is caused by the unclear contents of some RGB-D images and the poor utilization of image information by some methods. To solve the above problems, in this paper, we propose a Multi-scale Dense Fusion Network (MDFN) to improve RGB-D image understanding and enhance the performance of salient object detection. First, in the design of the encoder, we chose the Pyramid Pooling Transformer (P2T) as the backbone network of the encoder, and take advantage of the strong image understanding capability of the P2T to prevent the loss of important information when extracting RGB-D features. For the limitations such as less depth map information and noise interference, we propose the Depth Attention Enhancement Module (DAEM) to enhance the depth feature representation, and design the RGB-Depth Fusion Module (rdFM) to achieve RGB-D information matching. In the decoder design, to improve the utilization of image information, we propose the Dual-dense Fusion Module (DFM) to fully integrate the features of each layer and achieve multiple applications of the object information. Compared with 7 SOTA methods on 3 public datasets, MDFN achieves excellent performance on 4 evaluation metrics (Sα, Fβ, Eξ, M) and saliency maps, which proves the effectiveness of our method. © 2023 IEEE.

关键词： Object recognition

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Comparison of Deep Learning Methods for Bear-Presence detection at Conservation Center 3

Comparison of Deep Learning Methods for Bear-Presence Detect...

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3rd international conference on Electronic and Electrical Engineering and Intelligent system, ICE3IS 2023

作者： Riyadi, Slamet Azzahra Arifin, Yasmina Rizky Setiawan, Fadhilah Universitas Muhammadiyah Yogyakarta Department of Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350327762

Bears are one of the most well-known carnivorous creatures on the planet. Bears are a protected species;accordingly, numerous varieties of bears reside or are maintained at the conservation center. Because conservation officers must observe bear growth at the conservation center, a non-conventional solution is necessary to aid officers in monitoring the bear. In this research, we implemented two deep learning models, YOLO v7 and Mask R-CNN, to determine which model was more effective for bear detection. The R-CNN mask is the most accurate model, while YOLO v7 is the most recent version of YOLO. The results of the experiments implementing Mask R-CNN and YOLO v7 revealed that YOLO v7 performed better in detecting bears, with an average IoU (Intersection Over Union) of 0.89 and Mask R-CNN of 0.87. © 2023 IEEE.

关键词： Object detection

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"One Form" Empowers Digital Governance of University Services via Seq2Seq Model 3

"One Form" Empowers Digital Governance of University Service...

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3rd international conference on Digital Society and Intelligent systems, DSInS 2023

作者： Zhang, Limin Li, Lin Network Information Center Wuhan University of Technology Wuhan430070 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798350331387

In the digital transformation stage of universities, how to realize the digital governance of college services, is an interesting topic to solve the problems of data dispersion and repeated filling in. Those inconveniences are strongly posted by teachers and students. Therefore, "One Form" is a way to promote the modernization of university governance system and governance capacity. This paper mainly expounds the key issues of digital governance, how the "One Form" platform completes the digital governance of university services, and how to automatically match the corresponding data fields in different tables via Seq2Seq, a well known deep learning model. © 2023 IEEE.

关键词： Metadata

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systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry 3

Systematic Literature Review on Industry Revolution 4.0 to P...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Sowmya, P. Ravichandran, Sathish Kumar Rakshitha Department of Robotics and Artificial Intelligence Karnataka Nitte India Christ University School of Engineering and Technology Department of Computer Science Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning Karnataka Nitte India

ISBN: (纸本)9781665462167

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. © 2023 IEEE.

关键词： Industry 4.0

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3rd IEEE international conference on Social Sciences and intelligence Management, SSIM 2023

作者： Kaur, Sarabjot Bhatt, Priyanka C. Jindal Global Business School O.P. Jindal Global University Sonipat India Chaoyang University of Technology Department of Business Administration Taichung Taiwan

ISBN: (纸本)9798350327786

Information was retrieved from patent documents using a robust framework centered on the advanced DeBERTa model in this study. Emphasizing the crucial interplay between algorithmic selection and patent language intricacies, the developed method involved four epochs of model training. Significant enhancements were found through the Pearson correlation analysis. The proposed novel method improved model understanding. Cross-validation was conducted to ensure a balanced evaluation, and the contemplation of an ensemble approach was used for the model's refinement. String-matching algorithms validated the analysis of this research showing the model's efficacy in capturing relationships within patents. The research results allowed for algorithmic tailoring and applications in patent search. By fine-tuning, transferring learning exploration, and addressing biases, continual innovation in patent analysis and information retrieval is continued based on multi-modal integration. © 2023 IEEE.

关键词： String searching algorithms

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2023 3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

2023 3rd International Conference on Computer Science, Elect...

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3rd international conference on Computer Science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

ISBN: (纸本)9798350372052

The proceedings contain 182 papers. The topics discussed include: cable defect detection based on terahertz time-domain spectroscopy technology;improvement of multi-mechanism PBFT consensus algorithm based on attribute-based encryption grouping;design and implementation of indoor visual autonomous navigation system for quadrotor UAV based on binocular camera and IMU;LM-Net-Dual: a two branch spatiotemporal feature extraction model for dynamic gestures recognition;finite element simulation analysis of flexible wheel parameters of robot harmonic reducer;configuration and coordinated operation strategy of integrated energy substation for new-type power system construction;online adaptive prediction method for cloud computing workloads with bi-LSTM;a low-side-lobe transmissive antenna based on linear-to-circular polarization conversion metasurface;and design of multi-layer information security protection scheme for vehicle-to-grid system.

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Fault detection in Bottle Caps and Label Alignment Using Convolutional Neural Network 3

Fault Detection in Bottle Caps and Label Alignment Using Con...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Tatu, Sapana Indarchand Singh, Shashank Kumar Bansod, Saurabh Pal, Prashant National Institute of Electronics and Information Technology Department of EDT Aurangabad India National Institute of Electronics and Information Technology Aurangabad India

ISBN: (纸本)9781665494007

Quality control is one of the most crucial phases in the creation of any product. Product quality is the degree to which a product lives up to expectations. Due to flaws in bottle caps and label alignment, the quality of beverage products suffers. To overcome this problem, detection of fault via computer-based methods is a developing trend across many industries. In this paper by using Convolutional neural networks, image processing is used to detect fault in bottle caps which include loose caps, tilted caps, and missing caps and edge detection method is used to detect fault in label alignment which includes misplaced or tilted labels and inverted labels. The proposed model will improve precision and speed of fault detection. Due to the accuracy and time concession the adaptability of this machine learning method, the system can operate in a variety of lighting and backdrop conditions. © 2023 IEEE.

关键词： Computer vision

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U-NET and RCNN Ensembled Satellite Object detection and Segmentation 3

U-NET and RCNN Ensembled Satellite Object Detection and Segm...

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3rd international conference on Artificial intelligence and Smart Energy, ICAIS 2023

作者： Subhashini, G. Devi, Ajantha Niroshini Infantia, C. Karthi, M. Raghul, G. Shankar, S. Aravind St.Josephs Institute of Technology Chennai India AP3 Solutions Chennai India

ISBN: (纸本)9781665462167

Humans have always been curious to learn and explore new things and have achieved great things with that curiosity. "CURIOSITY IS THE WICK IN THE CANDLE OF LEARNING,"said William Arthur Ward. With the advancing technologies and advancing human needs, automation has become a leading technology in the IT sector. In the year of 1959, the first ever man-made satellite, which could take pictures was launched for the purpose of observing our planet. This led to the widely used technology in the year 2022, The Satellite images play a very crucial role in the n day-to-day activities of humans. It's because of satellite imagery, the weather forecasting capabilities of humans have improved drastically. People can sleep without worrying about the next upcoming storm as it can be detected with the help of satellite imagery and machine learning algorithms. Thus, precautions for the same can be taken even before the storm is formed. GPS system is another widely used technology used for security and exploration purposes. Satellite imagery with GPS technology gave another revolutionary use for humans, such as live location tracking in a graphical map, detected and captured by satellites. An example of such an application is Google Maps. Google Maps use various type of satellite images and machine learning algorithms to automate the various operations for public use. With the help of these technologies, this article will give an overview that how satellite imagery helps us to detect objects from satellite images and further segment images from satellite images using machine learning algorithms like RCNN with RPN and U-NET. © 2023 IEEE.

关键词： Object detection

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