机电工程技术2024，Vol.53Issue(6)：240-244,5.DOI:10.3969/j.issn.1009-9492.2024.06.055
基于YOLOv8和DeepLabv3+的指针仪表读数识别
Recognition of Point Meter Readings Based on YOLOv8 and DeepLabv3+
摘要
Abstract
Pointer instrument is widely used in the field of nuclear power as an important detection tool.In view of the low accuracy and detection rate of pointer instrument reading recognition under different types and distances,a method for instrument reading recognition based on YOLOv8 and DeepLabv3+is proposed.In order to improve the image input quality of the DeepLabv3+model,the YOLOv8 detector with fast reasoning speed and accurate positioning is selected to locate the instrument area and crop it as the input image for instrument recognition.In view of the low accuracy and slow detection rate of instrument pointer recognition,on the basis of the DeepLabv3+model,the backbone network is replaced with MobileNetv3,and the ECA+module is designed to replace its SE module,reducing model parameters while improving recognition accuracy.The quadruple upsampling of the decoder is replaced with two double upsampling,and the shallow feature map is spliced with the corresponding size feature of the encoder.The CBAM module is introduced to improve the accuracy of pointer segmentation.In the experimental stage,using self-made image dataset of nuclear power plant pointer instrument,the experimental results show that the method achieves a recognition accuracy of 94%at an instrument accuracy level of 2.5,with an average error of 1.542%and an average total time consumption of 0.57s,which has good performance.关键词
核电/指针式仪表/YOLOv8/DeepLabv3+/读数识别Key words
nuclear power/pointer instrument/YOLOv8/DeepLabv3+/reading recognition分类
机械制造引用本文复制引用
吴肖,赵洪泉,杨鹏,张景元,石波..基于YOLOv8和DeepLabv3+的指针仪表读数识别[J].机电工程技术,2024,53(6):240-244,5.基金项目
华能集团总部科技项目(HNKJ21-H22) （HNKJ21-H22）
