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基于多式联运特征选择和跨式联运Swin变压器的塑料废物识别
Tianchen Ji1, Huaiying Fang1, Rencheng Zhang1
1College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, Fujian, China.
Waste management (New York, N.Y.)
|November 27, 2024
概括
本研究介绍了在城市固体废物 (MSW) 分类中识别塑料废物的先进多式联运方法. 开发的关联SF-Swin变压器显著提高了塑料废物检测的准确性,有助于资源保护和污染预防工作.
科学领域:
- 环境科学 环境科学
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 城市固体废物 (MSW) 管理依赖于有效的塑料废物分类,以节约资源和防止污染.
- 与废物识别的单模式方法相比,多模式检测提供了更好的信息容量.
- 现有的超频谱特征选择和多式联运识别方法无法充分利用跨式联运信息.
研究的目的:
- 开发使用多式联运数据识别塑料废弃物的先进方法.
- 提高废物分类过程的效率和准确性.
- 为了解决当前超频谱特征选择和多模式识别技术的局限性.
主要方法:
- 构建两个RGB-超光谱图像 (RGB-HSI) 塑料废物多模式实例细分数据集.
- 基于激活权重函数的特征频段选择算法的建议,用于超频谱数据.
- 引入多式联网选择性特征网络 (SFNet) 和关联旋转变压器块,以实现跨式联网信息融合.
主要成果:
- 激活重量带选择算法有效地识别了有影响力的超频谱带.
- 关联SF-Swin变压器在塑料废物物体检测中获得了高F1分数97.85%和97.37%.
- 提出的方法证明了塑料废物的增强多式联运识别能力.
结论:
- 开发的多式联运方式显著推进了塑料废弃物在MSW分类中的识别.
- 激活重量函数和关联SF-Swin变压器为废物管理提供了高效和准确的解决方案.
- 创建的数据集和模型支持进一步研究智能废物分类和资源回收.
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