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开发基于深度学习和物联网的废物管理和分类系统

Zhikang Chen¹, Yao Xiao¹, Qi Zhou¹

¹Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.

Environmental monitoring and assessment

|December 26, 2024

概括

这项研究介绍了一种改进的深度学习模型,用于使用物联网和边缘计算高效的废物分类. 增强的YOLOv7微型模型实现了更高的精度,降低了计算成本,优化了资源恢复.

科学领域:

计算机科学计算机科学
环境科学环境科学
工程工程师工程师工程师

背景情况:

有效的废物分类对于可持续发展和资源回收至关重要.
目前的废物管理系统面临着劳动力成本和高效分类的挑战.
整合物联网 (IoT) 和边缘计算为自动化废物管理提供了潜力.

研究的目的:

利用深度学习开发一个高效准确的废物分类系统.
在复杂,现实的背景场景中提高废物分类准确性.
为了创建一个适合在边缘设备上部署的轻量级模型.

主要方法:

收集了可回收废物图像的数据集,这些图像具有多样化和复杂的背景.
开发了一个基于YOLOv7-tiny.tiny的改进深度学习模型.
整合了部分卷积 (PConv),坐标注意力 (CA) 和SIoU损失函数到YOLOv7-tiny架构中.
利用边缘计算进行实时垃圾分类和数据传输.

主要成果:

与原始模型相比,改进的YOLOv7微型模型显示出更高的准确性 (mAP@.5: +1.7%,mAP@.5:.95: +1.4%).
减少模型参数 (4.8%) 和浮点运算 (FLOP) (5%),使其更轻量化.
在Jetson Nano边缘设备上实现了110ms的平均推断时间,FPS为9.
该模型有效地处理废弃图像,具有现实的,杂乱的背景.

结论:

开发的废物分类系统,由改进的YOLOv7-tiny模型提供动力,对自动化废物管理有效.
该模型的轻量级设计和提高的准确性使其适合在现实世界废物收集系统中实际部署.
这种方法有助于最大限度地利用资源并降低废物管理中的运营成本.

关键词:

深度学习是一种深度学习.边缘计算是一种边缘计算.图像的分类图像的分类.物联网的物联网,就是物联网.废物管理废物管理垃圾分类垃圾分类垃圾分类

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