Real Time Power Equipment Meter Recognition Based on Deep Learning

Fan, Zizhu; Shi, Linrui; Xi, Chao; Wang, Hui; Song, Wang; Wu, Gaochang

doi:10.1109/tim.2022.3191709

Cited by 21 publications

(12 citation statements)

References 52 publications

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“…Guan et al [23] propose a lightweight three-stage detection framework consisting of a coarse region proposal (CRP) module, a lightweight railway obstacle detection network (RODNet), and a postprocessing stage for recognizing obstacles in a single-railway image. Fan et al [24] propose a lightweight meter recognition method that combines deep learning and traditional computer vision techniques for an automatic meter reading. Cai et al [25] propose a one-stage object detection framework based on YOLOv4 for object detection in autonomous driving.…”

Section: B Lightweight Object Detection Modelmentioning

confidence: 99%

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

Yue

Meng

2023

IEEE Trans. Instrum. Meas.

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The emergence of empty-dish recycling robots has alleviated problems, such as labor shortages, caused by an aging population. The detection and grasping of dishes play a crucial role in empty-dish recycling robots. However, due to the limited resources of edge devices, traditional object detection models require more space to store parameters and much computational overhead, limiting the development of empty-dish recycling robots. Therefore, this article proposes an ultralightweight dish detection model YOLO-GS for an empty-dish recycling robot. We use the modified CSPDarknet as the backbone structure and design an ultralightweight neck structure for efficient feature fusion. Meanwhile, we design a lightweight head structure for object classification and bounding box coordinate regression by combining ghost shuffle convolution (GSConv2D) and the anchor-free method. For the empty-dish recycling robot to grasp the dishes, we design a dish grasp point extraction algorithm using image processing. Finally, TensorRT is used to optimize and accelerate the model for efficient and intelligent detection of dishes on the NVIDIA Jetson Xavier NX. The experimental results show that YOLO-GS achieves 99.380% mean average precision (mAP) with a parameter amount of 0.606 M. The inference speed of the TensorRT-optimized YOLO-GS algorithm reaches 31.371 FPS, which meets the needs of real-time dish detection by the empty-dish recycling robot. The image of the empty-dish recycling robot demo is available at https://www.youtube.com/watch?v=pCBo1nzm3qU&t=22s.

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Section: B Lightweight Object Detection Modelmentioning

confidence: 99%

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

Yue

Meng

2023

IEEE Trans. Instrum. Meas.

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“…Statistics key points scope (08) end for (09) Use a square to enclose the scope (10) if (the square is beyond the bounds of the image = false) (11) Increase the square by a factor of k (12) end if (13) Cut out the square to form a smaller picture (14) Save this picture (15) end ALGORITHM 1: Image cropping.…”

Section: Coarse-fine-grained Feature Fusion Architecturementioning

confidence: 99%

“…Deep learning technology has excellent performance in target detection [2] and image recognition [3] and can be used to give equipment the ability to automatically recognize targets and enhance the intelligence and fexibility of the equipment. Nowadays, there are many deep learning models (e.g., VGG [4], ResNet [3], Fast-RCNN [5], and YOLO [6]) that are widely used for production defect detection [7], product quality control [8], and object recognition [9][10][11]. But these models have similar characteristics, that is, they require many learning samples to gain experience.…”

Section: Introductionmentioning

confidence: 99%

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

Tan

Xia

2023

International Journal of Intelligent Systems

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With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse-fine-grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence.

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“…The approach of Zuo et al [6] only detects the masks of pointers and dials by Mask R-CNN [7], and then corrects to the standard position to take readings according to the template, which lacks the detection of numerical information. The method proposed by Fan et al [8] obtains the key points of scale and pointer through UNet [9], which however requires templates for different dial models to complete readings. Most existing methods cannot directly obtain a meter reading from deep network output and need additional prior information, and meter readings can only be obtained from known meters.…”

Section: Introductionmentioning

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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Real Time Power Equipment Meter Recognition Based on Deep Learning

Cited by 21 publications

References 52 publications

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

Scale value guided Lite-FCOS for pointer meter reading recognition

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