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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81-90 订阅

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Efficient Optimization and Compression of Autonomous Vehicle Target detection Models 3

Efficient Optimization and Compression of Autonomous Vehicle...

引用

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

作者： Lin, Zhi-Rui Wang, Hwang-Cheng National Yilan University Department of Electronic Engineering Yilan Taiwan

ISBN: (纸本)9798350327786

Target detection is a crucial element of computer vision and often requires complex models and extensive computing resources. This inherent complexity poses significant challenges for use especially in the demanding field of autonomous vehicles. Thus, for the urgent need to reduce resource consumption while preserving the accuracy of target detection, we optimized the YOLO model by utilizing depthwise separable convolution. The model reduced resource usage without compromising detection performance. Additionally, quantitative compression techniques were incorporated to decrease the model size for easy deployment. The optimization strategy better-tailored target detection solutions to the specific requirements of autonomous vehicle applications. © 2023 IEEE.

关键词： Autonomous vehicles

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Design Mechanism of English Translation Calibration system Based on Artificial intelligence technology 3

Design Mechanism of English Translation Calibration System B...

引用

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

作者： Yang, Xuming Ji, Dandan Shu, Zhen School of Foreign Languages Changchun University of Finance and Economics Changchun130122 China

ISBN: (纸本)9798350327786

With the rapid development of artificial intelligence (AI) technology, language processing is revolutionarily transformed. English translation calibration system serves as a pivotal component in language communication with increasing attention. We designed an English translation calibration system based on AI technology. By integrating advanced algorithms and models, the system enhanced translation accuracy, intelligence, and user experience. We analyzed the fundamental algorithm based on Long Short-Term Memory (LSTM) networks. We established a translation quality estimation model based on Bidirectional Long Short-Term Memory (BiLSTM), encompassing quality vector extraction, contextual word vectors, and the integration of a translation quality estimation model. Finally, the overall implementation process of the English intelligent translation calibration system was presented in this article. The model provides a more intelligent and efficient solution for English translation calibration, thereby advancing the continual application and innovation of AI technology in language processing. © 2023 IEEE.

关键词： Long short-term memory

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Moving Object detection and Recognition Algorithm Based on Deep Learning 3

Moving Object Detection and Recognition Algorithm Based on D...

引用

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

作者： Li, Jipan Tian, Keqiang Jing, Shuzhi Xu, Ke Wang, Weiling Liu, Dejian Sun, Zhizhou State Grid Heze Power Supply Company Heze China State Grid Intelligence Technology Co. Ltd. Jinan China

ISBN: (纸本)9781665490788

Aiming at the problem that the deep learning detection framework based on video moving object detection cannot make full use of the motion information of the upper and lower frames, and the computational complexity is high, an improved SSD detection algorithm is proposed. The algorithm uses the lightweight network MobileNet as the basic network in the SSD model to reduce the time and computation spent in extracting image features, and establish the correlation between frames. Based on the Tensorflow deep learning framework, training is carried out on COCO (Common Objects in Context) dataset, and comparative experiments on moving object recognition accuracy and recognition speed are carried out. Experimental results show that the algorithm has good performance in terms of detection accuracy and speed. © 2023 IEEE.

关键词： Object detection

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Rice Field Pest Detector Based on Deep Learning and Embedded system 24

Rice Field Pest Detector Based on Deep Learning and Embedded...

引用

3rd international conference on Frontiers of Artificial intelligence and Machine Learning (FAIML)

作者： Li, Jiaqi Li, Zixiang Wen, Xin Li, Jia Zhang, Zhao Meng, Wei Liu, Sheng Huaibei Normal Univ Coll Comp Sci & Technol Huaibei 235000 Peoples R China

ISBN: (纸本)9798400709777

To improve the efficiency of rice field pest identification, reduce labor costs, and solve the problem of one-sided judgment in manual detection, a pest detection method based on deep learning and an embedded system was proposed and a portable instrument was designed. The detection model was first obtained by training 1500 rice field image datasets of pests through a YOLOv2-MobileNet network, which was then deployed into a device, and real-time detection of common rice paddy pests was realized. To reduce the impact of model deployment on the device performance, the detection accuracy and image capture frame rate were improved by adjusting the width of the MobileNet network. The results showed that the average detection accuracy of the model trained by the backbone network MobileNet-0.75 was 89.4%, 80.7%, 90.0%, 81.9%, and 83.6% for the locust, rice leaf roller, rice stem borer, rice green mirid nymph, and rice green adult, respectively, and the average image acquisition frame rate reached 35 frames per second. Real-time detection requirements were met. The design realizes real-time detection of five common rice field pests using embedded equipment and provides a reference value for the application of intelligent equipment for the detection of agricultural pests.

关键词： Embedded system K210 Pest detection YOLOv2

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Small object 3D detection algorithm based on lidar point cloud 3

Small object 3D detection algorithm based on lidar point clo...

引用

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

作者： Zhang, Qian Che, Hu Liu, Jun Liu, Ruijun Nanchang Hangkong University Department of Information Engineering Jiangxi Nanchang330063 China Jiangxi Rui East Machinery Co. Ltd Jiangxi Nanchang330000 China

ISBN: (纸本)9781665490788

Aiming at the problems that point cloud has few available features and low positioning accuracy of small object in 3D object detection process. A new 3D object detection algorithm CFPointPillars based on improved PointPillars is proposed. First, before the feature extraction of the pseudo-image, Coordinate attention(CA) is introduced to obtain the position perception and direction perception information of the small object. Secondly, Feature Pyramid Network (FPN) structure is introduced to integrate the extracted features to obtain the precise semantic information of the small object. Finally test on KITTI public data set;The experimental results show that in terms of BEV detection benchmark, 3D detection benchmark and Average orientation Similarity(AOS), the mAP of CFPointPillars detection algorithm for car, pedestrians and cyclists reaches 70.79%, 64.44% and 71.75% respectively, which is 1.93%, 0.87% and 2.29% improvement compared with original network PointPillars respectively. © 2023 IEEE.

关键词： Object detection

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Sea Ship Target detection Based On Improved ViBe Algorithm 3

Sea Ship Target Detection Based On Improved ViBe Algorithm

引用

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

作者： Huang, Hong Jia, Qingzhong Tao, Ximing Beijing Institute of Technology Key Laboratory of Dynamic and Control of Flight Vehicle Beijing China Aviation Transport Equipment Provincial Technology Innovation Center Shanxi China

ISBN: (纸本)9781665490788

Aiming at the problems of slow 'ghost image' elimination speed, ship trail interference, and complicated sea surface noise in the process of ViBe algorithm's ship target detection in the sea background. This paper proposes a ViBeDiff3C object detection algorithm which combines the frame difference method and the improved ViBe algorithm. Based on the frame-to-frame difference method, the ViBe algorithm is improved to detect ship targets, and the 'ghost' problem caused by the ViBe algorithm caused by moving targets in the first frame of the image is solved, thereby improving the update speed of the background template and achieving the goal of quickly eliminating 'ghosts'. Objective To improve the detection effect of the frame difference method by introducing the Canny operator into the frame difference method, and solve the problems of sea surface noise, ship wakes and holes, thereby improving the clarity and accuracy of the target. © 2023 IEEE.

关键词： Object recognition

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intelligence Modeling of Navigation system based on Prior Knowledge 3

Intelligence Modeling of Navigation System based on Prior Kn...

引用

3rd international conference on Smart Data intelligence, ICSMDI 2023

作者： Huang, Lei Fan, Shigang High-tech Institute Fan Gong-ting South Street on the 12# Shandong Qingzhou262500 China

ISBN: (纸本)9781665464871

The concept of mobile GIS is based on traditional GIS, using certain spatial positioning means, such as GPS, inertial navigation, etc., combined with embedded technology, using digital maps as the display background, and providing real-time positions of moving targets through basic wireless communication networks. Inspired by this background, this paper proposes the novel navigation system based on the prior knowledge. The portable navigation and positioning device can be realized by further integrating the PDA and also the positioning module on the hardware, for the core, namely, the inner framework, the Beidou system is selected. Then, the system is applied to the sample scenarios, it can be seen from the simulated UI that the designed model is user-friendly. © 2023 IEEE.

关键词： Navigation systems

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Research on Tunnel Crack detection Algorithm Based on Improved YOLOv5 3

Research on Tunnel Crack Detection Algorithm Based on Improv...

引用

3rd international conference on Communication technology and Information technology, ICCTIT 2023

作者： Yu, Jiale Zhang, Zhenhai School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China Gansu Province Artificial Intelligence and Graphics and Image Processing Engineering Research Center China

ISBN: (纸本)9798350359664

Aiming at the misjudgment, omission, and insufficient feature extraction ability of the existing deep learning for tunnel crack detection, an improved algorithm model based on YOLOv5 network is proposed. Firstly, the CBAM attention mechanism is introduced to focus on the image successively in channel dimension and spatial dimension, so as to highlight the crack features, and then the BotNet network structure is added into the backbone network of YOLOv5, and the location information between the crack features is interconnected by utilizing the combination of the convolutional neural network and the global self-attention mechanism in the BotNet network. The results show that the improved algorithm proposed in this paper can better extract the crack region information compared with the YOLOv5 model, can recognize cracks of different sizes, and has higher detection accuracy. © 2023 IEEE.

关键词： Deep learning

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Automatic detection and Classification of Diabetic Retinopathy using Modified UNET 3

Automatic Detection and Classification of Diabetic Retinopat...

引用

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

作者： Nageswari, P. Kandasamy, Manivel Shanmugam, Raju Suresh Kumar, A. Mahendra Institute of Technology Department of Mathematics Tamilnadu Namakkal India Gujarat Gandhinagar India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9781665462167

One of the most prevalent and important causes of blindness and vision impairment in the worldwide is diabetes-related retinal vascular disease. Automated Diabetic Retinopathy (DR) detection devices would therefore be very helpful for early detection, treatment, and preventing vision loss brought by DR. Recently, Deep Neural Networks (DNNs) have undergone a number of improvements in recent years to help address the aforementioned issue. Hence, this article formulated, a novel topology to detection and classification of DR using modified UNet topology. The proposed methodology displayed its performance on the Messidor data test, with average accuracy readings of 97.86% respectively. The effectiveness of the suggested methodology has been demonstrated through extensive testing and comparisons with baseline approaches. © 2023 IEEE.

关键词： Topology

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Car Body Damage detection system Using YOLOv7 3

Car Body Damage Detection System Using YOLOv7

引用

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

作者： Ramazhan, Muhammad Remzy Syah Bustamam, Alhadi Anwar, Rinaldi Universitas Indonesia Faculty of Mathematics and Natural Sciences Department of Mathematics Depok Indonesia Jakarta Indonesia

ISBN: (纸本)9798350327762

Car damage inspection is an important step when submitting car insurance claims. Currently, insurance companies manually gather car damage assessment reports. However, this approach takes a lot of time and is susceptible to fraud. Advances in data science and artificial intelligence offer solutions for automated systems for car damage detection. This system can detect and classify different types of car damage. Implementation of this system can reduce operational costs, save time, and minimize leakage. In this study, we proposed an object detection algorithm that is YOLOv7 to automate car damage detection. Three variations from the YOLOv7 algorithm will be used in this paper. There are YOLOv7-tiny, YOLOv7, and YOLOv7x. Our experiment shows that YOLOv7x achieved the greatest performance with a validation F1-score of 0.949 for car damage detection. © 2023 IEEE.

关键词： Damage detection

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