为了保持领先的竞争优势，中国蓝星（集团）股份有限公司不断致力于优化生产流程并提高能源效率。为了更好地了解所有12个工厂（遍布全国9个省）的情况，中国蓝星公司转为使用OSIsoft PI System。通过改进的性能监控和计算能力，中国蓝...为了保持领先的竞争优势，中国蓝星（集团）股份有限公司不断致力于优化生产流程并提高能源效率。为了更好地了解所有12个工厂（遍布全国9个省）的情况，中国蓝星公司转为使用OSIsoft PI System。通过改进的性能监控和计算能力，中国蓝星公司促进了生产，提高了效率并降低了成本。展开更多
With the rapid development of the Internet of Things(IoT),artificial intelligence,and big data,wastesorting systems must balance high accuracy,low latency,and resource efficiency.This paper presents an edge-friendly i...With the rapid development of the Internet of Things(IoT),artificial intelligence,and big data,wastesorting systems must balance high accuracy,low latency,and resource efficiency.This paper presents an edge-friendly intelligent waste-sorting system that integrates a lightweight visual neural network,a pentagonal-trajectory robotic arm,and IoT connectivity to meet the requirements of real-time response and high accuracy.A lightweight object detection model,YOLO-WasNet(You Only Look Once for Waste Sorting Network),is proposed to optimize performance on edge devices.YOLO-WasNet adopts a lightweight backbone,applies Spatial Pyramid Pooling-Fast(SPPF)and Convolutional Block Attention Module(CBAM),and replaces traditional C3 modules(Cross Stage Partial Bottleneck with 3 convolutions)with efficient C2f blocks(Cross Stage Partial Bottleneck with 2 Convolutions fast)in the neck.Additionally,a Depthwise Parallel Triple-attention Convolution(DPT-Conv)operator is introduced to enhance feature extraction.Experiments on a custom dataset of nine waste categories conforming to Shanghai’s sorting standard(7,917 images)show that YOLO-WasNet achieves a mean average precision(mAP50)of 96.8%and a precision of 96.9%,while reducing computational cost by 30%compared to YOLOv5s.On a Raspberry Pi 4B,inference time is reduced from 480 to 350 ms,ensuring real-time performance.This system offers a practical and viable solution for low-cost,efficient automated waste management in smart cities.展开更多
文摘With the rapid development of the Internet of Things(IoT),artificial intelligence,and big data,wastesorting systems must balance high accuracy,low latency,and resource efficiency.This paper presents an edge-friendly intelligent waste-sorting system that integrates a lightweight visual neural network,a pentagonal-trajectory robotic arm,and IoT connectivity to meet the requirements of real-time response and high accuracy.A lightweight object detection model,YOLO-WasNet(You Only Look Once for Waste Sorting Network),is proposed to optimize performance on edge devices.YOLO-WasNet adopts a lightweight backbone,applies Spatial Pyramid Pooling-Fast(SPPF)and Convolutional Block Attention Module(CBAM),and replaces traditional C3 modules(Cross Stage Partial Bottleneck with 3 convolutions)with efficient C2f blocks(Cross Stage Partial Bottleneck with 2 Convolutions fast)in the neck.Additionally,a Depthwise Parallel Triple-attention Convolution(DPT-Conv)operator is introduced to enhance feature extraction.Experiments on a custom dataset of nine waste categories conforming to Shanghai’s sorting standard(7,917 images)show that YOLO-WasNet achieves a mean average precision(mAP50)of 96.8%and a precision of 96.9%,while reducing computational cost by 30%compared to YOLOv5s.On a Raspberry Pi 4B,inference time is reduced from 480 to 350 ms,ensuring real-time performance.This system offers a practical and viable solution for low-cost,efficient automated waste management in smart cities.
