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

共 2097 条记录，以下是1-10 订阅

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2023 3rd international conference on detection technology and intelligence system

2023 3rd International Conference on Detection Technology an...

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2023 3rd international conference on detection technology and intelligence system, dtis 2023

The proceedings contain 29 papers. The topics discussed include: application of mobile laser measurement system in railway inspection;automatic detection of sewer defects based on YOLOV5;measurement method of countersunk rivet flatness based on visual 3D reconstruction;research on the minimum detection limit for measuring high-purity gases by gas chromatography;three-dimensional profilometry for tool wear area using modulation-based structured illumination microscopy;a Sobel-enhanced total focusing method using an ultrasonic linear array transducer for detecting wrinkles in multidirectional CFRPs;phase coherence factor-based ultrasonic annular array testing method for coarse-grained materials;a novel design and application of anti-biofouling system of buoy sensor based on sampling measuring method;miner pose detection algorithm based on improved YOLOv7 in the coal mine;and Environmentally friendly gas-filled cabinet trace SF gas detection technology based on GA-BP model.

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Automatic detection of sewer defects based on yolov5 3

Automatic detection of sewer defects based on yolov5

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2023 3rd international conference on detection technology and intelligence system, dtis 2023

作者： Tang, Xuefeng Zhao, Zekuan He, Chunlin School of Electronic Information Engineering China West Normal University Sichuan Province Nanchong637000 China

As cities modernize, sewer systems continue to expand. However, pipe problems may lead to accidents such as sewage overflow. The traditional sewerage pipe detection technology suffers from low accuracy and inefficiency and mainly relies on manual visual identification, which is prone to subjective errors. In this paper, a new Sewer-Yolov58 model is proposed to solve this problem, which is improved based on the original Yolov5 for real-time detection of sewerage problems to detect pipeline problems in time and is favorable for the subsequent repair work. Through comparison experiments with other models, the results show that the improved Sewer-Yolov58 model has a more efficient and accurate detection effect. © Published under licence by IOP Publishing Ltd.

关键词： Sewers

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An adaptive label assignment scheme for slender object detection 3

An adaptive label assignment scheme for slender object detec...

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2023 3rd international conference on detection technology and intelligence system, dtis 2023

作者： Yin, Zongyu Yu, Yiheng Hou, Bowen Shang, Penghui Hong, Yifan Yu, Yutian Liu, Ke Hangzhou ZhiYuan Research Institute Co. Ltd. Zhejiang Hangzhou310024 China

Slender objects have a large aspect ratio and are generally oriented, resulting in poor performance of current general detectors on slender object detection tasks. Therefore, an adaptive label assignment scheme for slender object detection is proposed in this paper. Specifically, the central axis prior to positive training samples is proposed to make the final position distribution of positive training samples more reasonable. Secondly, it is proposed that the number of positive training samples of slender objects could be further increased to solve the problem of positive training sample imbalance between slender objects and regular objects. Experimental results on the MS COCO dataset demonstrate the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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A Novel Intrusion detection system based on Machine Learning for Internet of Things (IoT) Devices 3

A Novel Intrusion Detection System based on Machine Learning...

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3rd international conference on Smart Data intelligence, ICSMDI 2023

作者： Chauhan, Dhwanil Shah, Margi Joshi, Harshil Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9781665464871

An exponential increase has been observed in the amount of IOT devices. The demand for an intrusion detection system has increased with the proliferation of IOT devices. An intrusion detection system is made of machine learning algorithms or a combination of machine learning algorithms. These algorithms are used to identify and classify intrusions. This study compares the results obtained by applying Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier on the CICIDS -17. © 2023 IEEE.

关键词： Internet of things

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Miner pose detection algorithm based on improved YOLOv7 in the coal mine 3

Miner pose detection algorithm based on improved YOLOv7 in t...

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2023 3rd international conference on detection technology and intelligence system, dtis 2023

作者： Wang, Shuqi Li, Junyu College of Communication and Information Engineering Xi'An University of Science and Technology Xi'an710054 China

The unsafe behavior of workers in underground mines is a major cause of accidents. Recently, deep learning technology has become more and more widely used in the computer field and has achieved remarkable results, providing new opportunities for the research of miner behavior recognition algorithms. To address the current problem of large computation volume and low detection efficiency of multi-person posture detection in underground mines, we propose an improved algorithm that introduces the GSConv convolution module into the YOLOv7-Pose model and replaces the ELEN-W module with the GSELEN module to accelerate model convergence. We also introduce a group-convolution reconstruction SPPCSPC module to reduce parameter load and calculation amount and reduce the original 17 joint points to 14 joints to further realize a lightweight model. The experimental results showed that the improved YOLOv7-Pose model in this study decreased to 135.7 MB, by about 18.8%, while maintaining model accuracy. Therefore, this improvement can achieve a lightweight algorithm and improve real-time posture detection of construction workers in underground mines while ensuring model accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Measurement method of countersunk rivet flatness based on visual 3D reconstruction 3

Measurement method of countersunk rivet flatness based on vi...

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2023 3rd international conference on detection technology and intelligence system, dtis 2023

作者： Sun, Jianyang Jia, Shuang Wang, Jin Zhu, Changfa Wang, Yunhao Shanghai Aerospace Electronic Technology Institute Shanghai201108 China

The flatness is a key indicator to evaluate the riveting quality of countersunk rivets on the skin of aircraft or rockets. Aiming at the deficiencies of current measurement methods used by aerospace manufacturers, a measurement method of countersunk rivet flatness based on visual 3D reconstruction is proposed. Through the zonal modulation of the structured light intensity of the projector, the image interference problem caused by the highly reflective skin of aircraft and rockets is solved. A rivet detection algorithm based on an arc feature is designed to realize the accurate identification of rivets in the image. A riveting surface reconstruction algorithm based on region segmentation and parallax constraint is researched, which greatly improves the efficiency of 3D reconstruction. A calculation method based on the local mean is used to improve the stability of the measurement results. The experimental results show that the measurement error of the proposed method is within ±30 μm, the standard deviation of repeated measurement is within 6 μm, and the time of measuring a single rivet is about 2 s. The accuracy, stability, and efficiency of this method meet the requirements. © Published under licence by IOP Publishing Ltd.

关键词： Rivets

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Design of a Drone that Applies Multisensor Information for the Early detection of Forest Fires 3

Design of a Drone that Applies Multisensor Information for t...

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3rd international conference on Robotics, Automation and Artificial intelligence (RAAI)

作者： Rivera Paredes, Hamil Hector Munos Suazo, William Ciro Acero Calderon, Justo Gabriel Chamorro Quijano, Sario Angel Univ Continental Dept Mechatron Engn Huancayo Peru

ISBN: (纸本)9798350307573;9798350307566

The article focuses on the design and development of a drone equipped with advanced technology for the early detection of wildfires, a major threat to the environment, human life and property. Early detection is essential for effective response. This drone has multiple advanced sensors, including thermal cameras, high-resolution cameras and gas sensors, allowing it to continuously and accurately monitor forest areas. Its autonomous and programmable operation capability makes it an efficient tool for regular patrols over critical areas. The fire detection system is based on real-time analysis of thermal and high-resolution images captured by the drone. Computer vision algorithms process these images to identify early signs of fire, such as changes in temperature, smoke or presence of fire. When a potential fire is detected, the drone notifies authorities immediately. The advanced technology applied in this drone has the potential to save lives, reduce property damage and preserve the environment. This innovative approach to early wildfire detection shows great potential for the protection of vulnerable ecosystems and the safety of communities.

关键词： fire mapping detection process system

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Quantitative detection of composite defects based on infrared technology and FA-LSTM 3

Quantitative detection of composite defects based on infrare...

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2023 3rd international conference on detection technology and intelligence system, dtis 2023

作者： Luo, Gui Wang, Xinyang Zhu, Jianmin Zhou, Yuqi Engineering Training Center Civil Aviation University of China Tianjin300300 China Chaoyang Flight Training Base of Tianjin Jefferson International Flight College Tianjin300300 China Faculty of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China

Given the difficulties in obtaining the defect data of composite materials and the low accuracy of defect size detection, this paper proposes a quantitative defect detection method for composite materials based on infrared technology and a firefly algorithm (FA)-optimized long short-term memory (LSTM) network. First, composite plates with different defects were periodically heated and photographed using a specific heat source and infrared camera to obtain the maximum surface temperature difference and the best detection time data of the composite plates. Then, the maximum temperature difference and the best detection time of each cycle of the material plate are weighted averages, and a vector model containing the defect feature information is established. Next, the optimal number of hidden neurons and the learning rate are obtained by the FA optimization of the LSTM model parameters. Finally, the eigenvector model is input into the FA-LSTM for fitting and outputting the defect diameter and depth to realize the quantitative detection of material plate defects. The experimental results show that the FA-LSTM can accurately identify the defect diameter and depth of the material plate, with an average relative error of only 2.59%. © Published under licence by IOP Publishing Ltd.

关键词： Specific heat

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Study on insulator defect detection based on improved YOLOv8 3

Study on insulator defect detection based on improved YOLOv8

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2023 3rd international conference on detection technology and intelligence system, dtis 2023

作者： Liu, Dengchao Shanghai Maritime University Shanghai China

Given the problems of poor detection effect in aerial insulator images resulting in poor detection effect, a multi-defect detection algorithm for aerial insulators based on improved YOLOv8 is proposed. The algorithm first changed the FPN structure to BiFPN by introducing the upsampling and downsampling connections in the upstream and downstream of the feature pyramid, to enhance the interaction and information flow between characteristics at different scales. Secondly, the SPPF is improved by using the LSKA attention mechanism to enhance the ability to extract multi-scale features. Finally, for the detection of tiny fine particles, this paper reheavys the decoupling head and the lightweight decoupling head, extracts the key information above the space and channels, and implements a lower inference latency while maintaining accuracy. The algorithm detects the normal glass insulator, glass sheet dirt, glass sheet defect, polymer sheet dirt, polymer sheet defect, and polymer insulator in the aerial image, and simultaneously detects the flashover of the insulator. The experimental results show that the network used in the insulator defect data set detection accuracy (mAP) reached 87.8%, which increased by 2.6% compared to the benchmark model, and the calculation amount (GFLOPs) decreased by 24%. At the same time, after lightweight model parameters are reduced by 32%, based on the accuracy of the subsequent deployment of lightweight requirements, we can better detect insulators of different scales and different conditions. © Published under licence by IOP Publishing Ltd.

关键词： Antennas

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Review of Network Anomaly detection in the High-speed Railway Signal system Based on Artificial intelligence 3

Review of Network Anomaly Detection in the High-speed Railwa...

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3rd IEEE international conference on Computer Communication and Artificial intelligence, CCAI 2023

作者： Li, Siyuan Wang, Jing Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350335262

The advent of network communication technology and associated equipment has significantly enhanced the operation efficiency and automation of the high-speed railway signal system. However, these open and complex networks also face increased security threats. To mitigate the risk of malicious network attacks, effective network anomaly detection methods are progressively being adopted. Artificial intelligence (AI), with its exceptional self-learning, environmental adaptability, and massive data processing capabilities, has emerged as the development trend for network anomaly detection in the high-speed railway signal system. This paper first provides an overview of the current state of network security in the high-speed railway signal system and examines potential network security threats. It then offers a detailed comparison of AI-based network anomaly detection methods. Lastly, it discusses future research directions in this field. © 2023 IEEE.

关键词： Network security

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