Multi-meter Intelligent Detection and Recognition Method under Complex Background

Ni, Tao; Miao, Haifeng; Wang, Linlin; Ni, Shui; Huang, Lan

doi:10.23919/ccc50068.2020.9189383

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…Cai et al [36] proposed a deep CNN-based pointer meter recognition method using virtual samples. Ni et al [37] detected the location and category of the meter by SSD [38], then employed landmark detection to obtain key points, which contain the center point, zero point, full-scale point and endpoint of the meter. Sun et al [39] utilized YOLOv4 [40] to locate the meter, adopted Anam-Net [41] for semantic segmentation to extract the pointer, employed CRAFT [42] and E2E-MLT [43] for recognizing scale values and units, applied polar coordinate transformation (PCT) for scale regions, and deploy a lightweight CNN to locate the main scale line, followed by the computation of the reading data.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Scale value guided Lite-FCOS for pointer meter reading recognition

Wang,

Tian,

et al. 2023

Meas. Sci. Technol.

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As intelligent power grid construction advances, substation inspection becomes crucial, particularly in identifying meter readings.Existing meter reading methods are mainly based on the relationship between pointer and scale. However, these methods commonly suffer from the issue of over-relying on prior reading information, limiting readings to known meters.Hereby in this paper, we propose a method reaching more accurate and robust performance for meter reading by utilizing the unnoticed scale value.We determine the meter pointer direction with the aid of Hough transform and the pointer distribution.To detect meters from the scene as well as obtain scale values and pointers from the meter dial, we build an object detection network, named Lite-FCOS, whose backbone adopts a fast global context network (FGCNet) that is lightweight and is of powerful feature extraction capabilities.For training these, meter dial detection dataset (MDDD) and dial reading information dataset (DRID) are constructed.Lite-FCOS achieves 94.4 mAP50 and 96.7 mAP50 on the above two datasets with only 4.2M parameters and 56.2 FPS.The entire pointer meter reading recognition process only spends 52 ms on an RTX 3080Ti with a successful meter reading rate (SMR rate) of 89.6%, which indicates that the proposed method achieves promising accuracy and speed.

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