轻量级深度神经网络:使用基于深度可分离卷积的ICBAM优化车辆分类
Qifeng Niu1, Jinhui Han1, Zhen Sui2
1School of Physics and Telecommunication Engineering, Zhoukou Normal University, Zhoukou, China.
PloS one
|November 21, 2025
概括
本研究介绍了DSICBAMNet,这是一种轻量级的深度神经网络,用于智能交通系统中高效的车辆分类. 它实现了高精度 (在MIO-TCD上达到97.36%,在斯坦福汽车上达到96.51%) 并提高了计算效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 智能运输系统 智能运输系统
背景情况:
- 车辆分类对于智能运输系统至关重要.
- 现有的深度学习模型面临着计算效率和概括性的挑战.
- 实际部署需要轻量级和准确的模型.
研究的目的:
- 提出DSICBAMNet,一个新的轻量级和高效的深度神经网络用于车辆分类.
- 与现有模型相比,提高计算效率和概括能力.
- 在基准数据集上验证模型的性能.
主要方法:
- 开发了DSICBAMNet,集成深度可分离卷积 (DSC) 和改进的卷积块注意模块 (ICBAM).
- DSC可以降低计算复杂性和参数.
- 通过dropout和优化注意力机制,ICBAM提高了过阻力和特征加权.
主要成果:
- 在MIO-TCD数据集 (286个样本) 中,DSICBAMNet的准确度达到97.36%,在斯坦福汽车数据集 (1060个样本) 中达到96.51%.
- 与五个经典模型 (例如,AlexNet,MobileNetV2) 相比,表现出优越的性能.
- 格拉德-CAM和混矩阵分析证实了对关键地区的有效关注和一致的分类.
结论:
- 在智能运输中,DSICBAMNet为高效准确的车辆分类提供了一个有前途的解决方案.
- 该模型的轻量级设计和高性能使其适用于资源有限的环境.
- 在智能运输场景中验证了实际应用性和价值.
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