发布求助
文献互助 智能选刊 最新文献

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)最新文献

筛选
英文 中文
Detecting Floodwater on Roadways from Image Data Using Mask-R-CNN 利用Mask-R-CNN从图像数据中检测道路洪水
2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-08-01 DOI: 10.1109/INISTA49547.2020.9194655
S. Sarp, M. Kuzlu, M. Cetin, Cem Sazara, Özgür Güler
{"title":"Detecting Floodwater on Roadways from Image Data Using Mask-R-CNN","authors":"S. Sarp, M. Kuzlu, M. Cetin, Cem Sazara, Özgür Güler","doi":"10.1109/INISTA49547.2020.9194655","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194655","url":null,"abstract":"Object detection and segmentation algorithms evolved significantly in the last decade. Simultaneous object detection and segmentation paved the way for real-time applications such as autonomous driving. Detection and segmentation of (partially) flooded roadways are important inputs for vehicle routing and traffic management systems. This paper proposes an automatic floodwater detection and segmentation method utilizing the Mask-R-CNN algorithm - a deep learning algorithm belonging to Region-Based Convolutional Neural Networks (R-CNN) family of models for object detection and semantic segmentation. As the latest evolution in the R-CNN family, Mask-R-CNN fuses localization, classification, and segmentation in a compact and fast algorithm. To train the model, manually labeled images with urban, suburban, and natural settings are used. The performance of the algorithm is assessed in accurately detecting the floodwater captured in images. The results show that the proposed floodwater detection and segmentation perform better than previous studies.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125999166","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: 12
Sparsity in Reservoir Computing Neural Networks 储层计算神经网络的稀疏性
2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2020-06-04 DOI: 10.1109/INISTA49547.2020.9194611
C. Gallicchio
{"title":"Sparsity in Reservoir Computing Neural Networks","authors":"C. Gallicchio","doi":"10.1109/INISTA49547.2020.9194611","DOIUrl":"https://doi.org/10.1109/INISTA49547.2020.9194611","url":null,"abstract":"Reservoir Computing (RC) is a well-known strategy for designing Recurrent Neural Networks featured by striking efficiency of training. The crucial aspect of RC is to properly instantiate the hidden recurrent layer that serves as dynamical memory to the system. In this respect, the common recipe is to create a pool of randomly and sparsely connected recurrent neurons. While the aspect of sparsity in the design of RC systems has been debated in the literature, it is nowadays understood mainly as a way to enhance the efficiency of computation, exploiting sparse matrix operations. In this paper, we empirically investigate the role of sparsity in RC network design under the perspective of the richness of the developed temporal representations. We analyze both sparsity in the recurrent connections, and in the connections from the input to the reservoir. Our results point out that sparsity, in particular in input-reservoir connections, has a major role in developing internal temporal representations that have a longer short-term memory of past inputs and a higher dimension.","PeriodicalId":124632,"journal":{"name":"2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122731655","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: 6
首页 上一页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信