The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021最新文献_第2页

A Survey of Small Object Detection Based on Deep Learning 基于深度学习的小目标检测研究进展

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.009

Zhenghua Zhang, Jiang Ling, Qingqing Hong

{"title":"A Survey of Small Object Detection Based on Deep Learning","authors":"Zhenghua Zhang, Jiang Ling, Qingqing Hong","doi":"10.12792/icisip2021.009","DOIUrl":"https://doi.org/10.12792/icisip2021.009","url":null,"abstract":"As a basic visual recognition problem in computer vision, object detection has made great progress based on traditional manual features and deep learning algorithms. However, researches on small object detection ha ve only begun to appear in recent years, which has become a hot and difficult point in the field and most of them are improved on the basis of existing object detection algorithms to enhance the detection accuracy. With the rapid development of deep learning, small object detection based on deep learning has made great progress, which has wide application requirements in the fields of automatic driving, remote sensing image detection, criminal investigation and other fields, so the research on small object detection has strong practical values. In this paper, the existing research on small target detection is reviewed in detail. Firstly, the existing algorithms are divided into one stage and two stages according to the number of detection stages, and then the characteristics of these algorithms are analyzed; Secondly, the small object detection datasets commonly used are introduced. Finally, the challenges of small object detection are summarized, and the future research directions are prospected.","PeriodicalId":431446,"journal":{"name":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116976727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Rice Disease Identification System Using Lightweight MobileNetV2 基于轻量级MobileNetV2的水稻病害识别系统

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.007

Zhenghua Zhang, Yifeng Gu, Qingqing Hong

{"title":"Rice Disease Identification System Using Lightweight MobileNetV2","authors":"Zhenghua Zhang, Yifeng Gu, Qingqing Hong","doi":"10.12792/icisip2021.007","DOIUrl":"https://doi.org/10.12792/icisip2021.007","url":null,"abstract":"Rice is one of the main food crops in China, and rice diseases have become an important factor influencing the increase in food production losses in China. Traditional manual identification of rice diseases is time-consuming and labor-intensive. Machine learning algorithms have improved this problem and have been applied to the field of smart agriculture. The convolutional neural network (CNN) in deep learning has a significant effect on rice disease recognition relying on the characteristics of automatically extracting features. Aiming at five major rice diseases such as sheath blight, rice blast, bacterial leaf blight, rice smut and brown spot, this paper proposed a rice disease identification system using lightweight MobileNetV2. The identification results are uploaded and saved to the cloud database. Based on the lightweight model MobileNetV2, the system uses the channel pruning method to further compress the model. Compared with the original model, the memory usage has been reduced by 74%, the number of floating-point operations per second (FLOPS) has been reduced by 49%, the number of parameters has been reduced by 50%, and the accuracy of rice disease identification has increased by 0.16% to 90.84%.","PeriodicalId":431446,"journal":{"name":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124753389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A 3D Object Detection Framework for Intelligent Driving using YOLOv4 基于YOLOv4的智能驾驶三维目标检测框架

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.004

Zhen Li, Yuren Du, Qingqing Hong, S. Serikawa, Lifeng Zhang

引用次数: 0

Method for Estimation of Position of an Object in a Room Using Cross-Line Laser 用交叉线激光估计房间中物体位置的方法

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.019

Masaya Okamoto, Shiyuan Yang, S. Serikawa

引用次数: 0

Exploration of Sensor-Based Activity Recognition Based on Time Series Feature Extraction 基于时间序列特征提取的传感器活动识别研究

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.023

Wen-Hui Chen, Ting Chen, Cheng-Han Tsai

引用次数: 1

Proposal of a Method to Automatically Identify a Sensor as Energy Conversion or Energy Control 提出一种自动识别传感器为能量转换或能量控制的方法

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.029

Kei Sato, S. Serikawa

{"title":"Proposal of a Method to Automatically Identify a Sensor as Energy Conversion or Energy Control","authors":"Kei Sato, S. Serikawa","doi":"10.12792/icisip2021.029","DOIUrl":"https://doi.org/10.12792/icisip2021.029","url":null,"abstract":"There are three output types of sensors, which represent a voltage output type sensor, a current output type sensor, and a resistance change type sensor. If the output type is different, the detection circuit is also different. In the previous study, one unique analog detection circuit that can measure the output of different sensors has been proposed. However, the circuit had a switch and had to be switched manually. Therefore, the user needed to know in advance what type of output the sensor had. In this study, the switch is automatically switched according to the sensor type. We classify sensors into energy conversion type (voltage output type and current output type) and energy control type (resistance change type), and propose a method to automatically identify them. Using three types of sensors, we experimentally investigated whether they could be identified correctly. As a result, it became clear that any sensor can be automatically identified. For this reason, we do not need to know the type of sensor in advance. The switch is automatically switched according to the type of sensor. This makes it possible to operate the sensor correctly simply by connecting the sensor to one circuit.","PeriodicalId":431446,"journal":{"name":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129322364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Development of AI Predictive Maintenance System using IoT Sensing 基于物联网传感的人工智能预测性维护系统开发

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.025

K. Hayakawa, A. Heima, M. Ozaki, Satoshi Yoshida

引用次数: 0

Automatic Opening and Closing System for Windows and Curtains Using Fuzzy 采用模糊控制的门窗自动启闭系统

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.012

Toshimasa Noda, Yuhki Kitazono

引用次数: 0

Effects of Listening of "Dengaku" Music (One of Japanese Traditional Music) to the Changes of Heart Rate Variability 日本传统音乐“登歌”音乐对心率变异性变化的影响

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.016

Satoshi Watanabe, Takahiro Sugiyama, Naofumi Nakaya, N. Shirahama

{"title":"Effects of Listening of \"Dengaku\" Music (One of Japanese Traditional Music) to the Changes of Heart Rate Variability","authors":"Satoshi Watanabe, Takahiro Sugiyama, Naofumi Nakaya, N. Shirahama","doi":"10.12792/icisip2021.016","DOIUrl":"https://doi.org/10.12792/icisip2021.016","url":null,"abstract":"To investigate the effects of listening to music, analyses of heart rate variability (HRV) by discrete Fourier transform (DFT) have been widely conducted. These methods are useful to estimate the autonomic nervous system activity (sympathetic or parasympathetic activity). And to examine the effects of listening to “ Dengaku ” music, one experiment is carried out. Eleven healthy Japanese people participate in the experiment, and one piece of “ Dengaku ” music is employed as the test piece. All participants are asked to listen to the test piece individually, and their HRV are recorded and analyzed by DFT. The experiment result shows that the sympathetic nerve activities of all participants tend to decrease when they are listening to the test piece. This fact is a rare case (usually, there are some participants whose sympathetic nerve activities tend to increase when they are listening to music). , variation of LF/HF indexes are reduced) are obtained when participants are listening to “ Dengaku ” music. However, to obtain reliable conclusions, conducting more investigations would be especially important.","PeriodicalId":431446,"journal":{"name":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124135562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Road Surface Identification Method Improved Early Detection Performance Using Ultrasonic Sensors 一种利用超声传感器提高路面早期检测性能的方法

The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021 Pub Date : 1900-01-01 DOI: 10.12792/icisip2021.021

Yudai Kubo, Hidemitsu Arimura, Shenglin Mu, S. Nishifuji, Shota Nakashima

{"title":"A Road Surface Identification Method Improved Early Detection Performance Using Ultrasonic Sensors","authors":"Yudai Kubo, Hidemitsu Arimura, Shenglin Mu, S. Nishifuji, Shota Nakashima","doi":"10.12792/icisip2021.021","DOIUrl":"https://doi.org/10.12792/icisip2021.021","url":null,"abstract":"Currently, the number of elderly people in the world is increasing. As a result, the number of accidents involving elderly people falling when movement is increasing. Development of mobility support systems is necessary for them to move safely. Therefore, the system was developed to help wheelchairs move by identifying the type of road surface in front of them. The system used ultrasonic sensors attached to the wheelchair to identify the road surface. Then, the method for identifying four types of road surfaces using Support Vector Machines (SVM) was proposed for the road surface identification method that constitutes the mobility support system. However, in the previous study, only the case where measured road surface didn't change was verified. This made it impossible to make early identification when the road surface changed during measurement. In this paper, the new road surface identification method using ultrasonic sensors is proposed. The proposed method makes it possible to identify the boundary of a road surface when it changes. In addition, the method improves the early detection performance. In order to verify the performance of early identification road boundary, two road surfaces with different roughness were measured in succession. As a result, the proposed method was able to identify at before entering the road boundary. This confirms the effectiveness of the road surface identification method that takes the time series into account for sample obtainment.","PeriodicalId":431446,"journal":{"name":"The Proceedings of The 8th International Conference on Intelligent Systems and Image Processing 2021","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126962460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0