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SR-SqueezeNet:一种基于光滑激活函数的轻量级超频谱油污识别模型

Jiaye Li¹, Yi Ma², Yonggang Ji³

¹First Institute of Oceanology, Ministry of Natural Resources, Qingdao 266061, China; College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China.

Marine pollution bulletin

|December 5, 2024

概括

一个新的轻量级超频谱识别模型SR-SqueezeNet,增强了无人驾驶飞行器 (UAV) 的石油泄漏检测. 这种模型显著减少了尺寸和参数,同时提高了实时灾难响应的准确性.

科学领域:

环境监测环境监测
遥感技术遥感技术遥感技术
环境科学中的人工智能

背景情况:

实时识别石油泄漏对于灾难应急响应至关重要.
无人驾驶飞行器 (UAV) 为石油泄漏提供灵活,快速和低成本的监测解决方案.
为无人机开发轻量级模型对于高效的机载处理至关重要.

研究的目的:

提出一种新的轻量级超光谱识别模型,用于使用无人机检测漏油.
为提高无人机应用的油污识别模型的准确性和减少计算负载.
评估与现有解决方案对比拟的模型的性能.

主要方法:

开发SR-SqueezeNet,这是一个基于SqueezeNet架构的轻量化超频谱识别模型.
将自定义的光滑类型激活功能Smooth-ReLU集成到模型中.
实验验证使用多维空中图像对石油泄漏的验证.

主要成果:

在模型轻量化和提取精度方面,SR-SqueezeNet表现出卓越的性能.
与传统的SqueezeNet.Net相比,识别准确度提高了1.92%.
模型参数减少了75.11%,模型大小从26.46 MB减少到12.15 MB.

结论:

该SR-SqueezeNet模型在轻量化和检测准确性方面取得了重大进展,用于通过无人机监测漏油情况.
该模型显示了在无人机的实时油污泄漏检测系统中实际应用的巨大潜力.
开发的模型解决了在灾害管理中对有效和准确的漏油识别的迫切需要.

关键词:

深度学习是一种深度学习.轻量化轻量化轻量化轻量化轻量化海洋遥感海洋遥感石油泄漏的识别石油泄漏的识别这就是SqueezeNet.无人机无人驾驶飞行器 (UAV) 是一种无人机.

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