Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper pro...Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper proposes a recognition method based on YOLOv8 and DeepLabv3+. To improve the image input quality of the DeepLabv3+ model, the YOLOv8 detector is used to quickly locate the instrument region and crop it as the input image for recognition. To enhance the accuracy and speed of pointer recognition, the backbone network of DeepLabv3+ was replaced with Mo-bileNetv3, and the ECA+ module was designed to replace its SE module, reducing model parameters while improving recognition precision. The decoder’s fourfold-up sampling was replaced with two twofold-up samplings, and shallow feature maps were fused with encoder features of the corresponding size. The CBAM module was introduced to improve the segmentation accuracy of the pointer. Experiments were conducted using a self-made dataset of pointer-style instruments from nuclear power plants. Results showed that this method achieved a recognition accuracy of 94.5% at a precision level of 2.5, with an average error of 1.522% and an average total processing time of 0.56 seconds, demonstrating strong performance.展开更多
为提升远程电表数据采集系统的覆盖能力与终端续航性能,设计并实现一套机器类通信长期演进(Long Term Evolution for Machines,LTE-M)技术的远程抄表系统。通过构建模块化架构、优化通信协议与部署策略,有效解决传统通用分组无线服务(Ge...为提升远程电表数据采集系统的覆盖能力与终端续航性能,设计并实现一套机器类通信长期演进(Long Term Evolution for Machines,LTE-M)技术的远程抄表系统。通过构建模块化架构、优化通信协议与部署策略,有效解决传统通用分组无线服务(General Packet Radio Service,GPRS)技术与远距离无线电(Long Range Radio,LoRa)方案在信号盲区多、功耗高、传输延迟大等方面的问题。实际运行结果显示,系统通信成功率达99.2%,日均上报延迟控制在3.5 min以内,终端续航时间超过10年。展开更多
文摘Pointer instruments are widely used in the nuclear power industry. Addressing the issues of low accuracy and slow detection speed in recognizing pointer meter readings under varying types and distances, this paper proposes a recognition method based on YOLOv8 and DeepLabv3+. To improve the image input quality of the DeepLabv3+ model, the YOLOv8 detector is used to quickly locate the instrument region and crop it as the input image for recognition. To enhance the accuracy and speed of pointer recognition, the backbone network of DeepLabv3+ was replaced with Mo-bileNetv3, and the ECA+ module was designed to replace its SE module, reducing model parameters while improving recognition precision. The decoder’s fourfold-up sampling was replaced with two twofold-up samplings, and shallow feature maps were fused with encoder features of the corresponding size. The CBAM module was introduced to improve the segmentation accuracy of the pointer. Experiments were conducted using a self-made dataset of pointer-style instruments from nuclear power plants. Results showed that this method achieved a recognition accuracy of 94.5% at a precision level of 2.5, with an average error of 1.522% and an average total processing time of 0.56 seconds, demonstrating strong performance.
文摘为提升远程电表数据采集系统的覆盖能力与终端续航性能,设计并实现一套机器类通信长期演进(Long Term Evolution for Machines,LTE-M)技术的远程抄表系统。通过构建模块化架构、优化通信协议与部署策略,有效解决传统通用分组无线服务(General Packet Radio Service,GPRS)技术与远距离无线电(Long Range Radio,LoRa)方案在信号盲区多、功耗高、传输延迟大等方面的问题。实际运行结果显示,系统通信成功率达99.2%,日均上报延迟控制在3.5 min以内,终端续航时间超过10年。
