拓宽注意力增强型阿特拉斯卷积网络,在资源限制条件下实现高效嵌入式视觉应用

Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall N. Niles, Ken Pathak, Joe Tom
{"title":"拓宽注意力增强型阿特拉斯卷积网络,在资源限制条件下实现高效嵌入式视觉应用","authors":"Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall N. Niles, Ken Pathak, Joe Tom","doi":"10.1002/aisy.202300480","DOIUrl":null,"url":null,"abstract":"Onboard image analysis enables real‐time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision‐making critical for time‐sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time‐sensitive inference. This article introduces the widened attention‐enhanced atrous convolution‐based efficient network (WACEfNet), a new convolutional neural network designed specifically for real‐time visual classification challenges using resource‐constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width‐wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive overhead. Extensive benchmarking confirms state‐of‐the‐art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high‐fidelity real‐time analytics across a variety of embedded perception paradigms.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"8 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Widened Attention‐Enhanced Atrous Convolutional Network for Efficient Embedded Vision Applications under Resource Constraints\",\"authors\":\"Meftahul Ferdaus, Mahdi Abdelguerfi, Kendall N. Niles, Ken Pathak, Joe Tom\",\"doi\":\"10.1002/aisy.202300480\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Onboard image analysis enables real‐time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision‐making critical for time‐sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time‐sensitive inference. This article introduces the widened attention‐enhanced atrous convolution‐based efficient network (WACEfNet), a new convolutional neural network designed specifically for real‐time visual classification challenges using resource‐constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width‐wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive overhead. Extensive benchmarking confirms state‐of‐the‐art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high‐fidelity real‐time analytics across a variety of embedded perception paradigms.\",\"PeriodicalId\":7187,\"journal\":{\"name\":\"Advanced Intelligent Systems\",\"volume\":\"8 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advanced Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/aisy.202300480\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/aisy.202300480","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

机载图像分析可实现无人平台(包括空中、地面和水上无人机)的实时自主能力。在嵌入式系统上进行分类,而不是传输数据,可实现快速感知和决策,这对搜救、危险环境探索和军事行动等时间敏感型应用至关重要。要充分发挥这些系统的潜力,需要专门的深度学习解决方案,在准确性和计算效率之间取得平衡,以满足时间敏感型推理的需要。本文介绍了基于卷积的宽注意力增强型高效网络(WACEfNet),这是一种新的卷积神经网络,专为使用资源受限的嵌入式设备应对实时视觉分类挑战而设计。WACEfNet 建立在 EfficientNet 的基础上,集成了创新的宽度特征处理、atrous 卷积和注意力模块,在不增加过多开销的情况下提高了表示能力。广泛的基准测试证实了 WACEfNet 在航空成像应用方面的一流性能,同时也适合嵌入式部署。精度和速度的提高证明了定制化深度学习技术在为无人机和相关嵌入式系统释放新功能方面所具有的潜力,而无人机和相关嵌入式系统在尺寸、重量和功耗方面都有严格的限制。这项研究提供了一个优化框架,结合了扩大的残差学习和注意力机制,以满足各种嵌入式感知范例对高保真实时分析的独特需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Widened Attention‐Enhanced Atrous Convolutional Network for Efficient Embedded Vision Applications under Resource Constraints
Onboard image analysis enables real‐time autonomous capabilities for unmanned platforms including aerial, ground, and aquatic drones. Performing classification on embedded systems, rather than transmitting data, allows rapid perception and decision‐making critical for time‐sensitive applications such as search and rescue, hazardous environment exploration, and military operations. To fully capitalize on these systems’ potential, specialized deep learning solutions are needed that balance accuracy and computational efficiency for time‐sensitive inference. This article introduces the widened attention‐enhanced atrous convolution‐based efficient network (WACEfNet), a new convolutional neural network designed specifically for real‐time visual classification challenges using resource‐constrained embedded devices. WACEfNet builds on EfficientNet and integrates innovative width‐wise feature processing, atrous convolutions, and attention modules to improve representational power without excessive overhead. Extensive benchmarking confirms state‐of‐the‐art performance from WACEfNet for aerial imaging applications while remaining suitable for embedded deployment. The improvements in accuracy and speed demonstrate the potential of customized deep learning advancements to unlock new capabilities for unmanned aerial vehicles and related embedded systems with tight size, weight, and power constraints. This research offers an optimized framework, combining widened residual learning and attention mechanisms, to meet the unique demands of high‐fidelity real‐time analytics across a variety of embedded perception paradigms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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