基于卷积神经网络的旅游图像分类SqueezeNet - 以斯伦德西湖为例
Lantao Xu1, Xuegang Chen1, Xinlu Yang2
1School of Geographical Science and Tourism, Xinjiang Normal University, Urumqi, Xinjiang, China.
PloS one
|January 29, 2024
概括
这项研究使用轻量级卷积神经网络 (CNN) 增强了旅游图像分类. 改进的SqueezeNet模型在小文件大小下达到85.75%的准确性,有助于旅游资源开发.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 旅游学研究 旅游学研究
背景情况:
- 仅仅是人类视觉不足以深入分析游客对旅游资源的看法.
- 卷积神经网络 (CNN) 为自动化旅游图像分类提供了新的可能性.
研究的目的:
- 为了提高旅游图像分类的准确性和效率.
- 开发一种轻量级模型来对旅游图像进行分类.
主要方法:
- 这项研究使用了3740张Slender West Lake旅游图像的数据集.
- 选择并修改了现有的轻量级CNN模型SqueezeNet.
- 改进后的模型在旅游图像数据集上进行了训练和验证.
主要成果:
- 增强的SqueezeNet模型实现了85.75%的验证准确度.
- 最终的模型尺寸减少到2.64 MB.
- 结果显示了高精度的分类与降低参数.
结论:
- 改进的SqueezeNet模型为旅游图像分类提供了有效的解决方案.
- 这项研究为旅游形象研究和资源规划提供了科学参考.
- 这项研究表明了旅游资源开发和规划的新方向.
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