A robust approach to reading recognition of pointer meters based on improved mask-RCNN

Zuo, Lin; He, Ping; Zhang, Changhua; Zhang, Zhehan

doi:10.1016/j.neucom.2020.01.032

Cited by 73 publications

(52 citation statements)

References 18 publications

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“…We also focus on studies related to digit-based meters, even though there are some recent works that addressed the recognition of dial meters [13], [14], [23]. Such works usually explore the angle between the pointer and the dial to perform the reading.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, image-based techniques for AMR are much needed, especially taking into account that it is not feasible to quickly replace old meters with smart ones [10]- [12]. The idea behind image-based AMR, which is an specific scenario of scene text detection and recognition, is that the aforementioned inspection can be carried out automatically, reducing mistakes introduced by the human factor and saving manpower [3], [13]. As pointed out by Salomon et al [14], the consumers themselves can capture photos of meters using a mobile device (e.g., a cell phone or a tablet).…”

Section: Introductionmentioning

confidence: 99%

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Towards Image-Based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

et al. 2021

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Existing approaches for image-based Automatic Meter Reading (AMR) have been evaluated on images captured in well-controlled scenarios. However, real-world meter reading presents unconstrained scenarios that are way more challenging due to dirt, various lighting conditions, scale variations, in-plane and out-of-plane rotations, among other factors. In this work, we present an end-to-end approach for AMR focusing on unconstrained scenarios. Our main contribution is the insertion of a new stage in the AMR pipeline, called corner detection and counter classification, which enables the counter region to be rectified -as well as the rejection of illegible/faulty meters -prior to the recognition stage. We also introduce a publicly available dataset, called Copel-AMR, that contains 12,500 meter images acquired in the field by the service company's employees themselves, including 2,500 images of faulty meters or cases where the reading is illegible due to occlusions. Experimental evaluation demonstrates that the proposed system, which has three networks operating in a cascaded mode, outperforms all baselines in terms of recognition rate while still being quite efficient. Moreover, as very few reading errors are tolerated in real-world applications, we show that our AMR system achieves impressive recognition rates (i.e., ≥ 99%) when rejecting readings made with lower confidence values.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Image-Based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Then, masks are made by graphical image semantic segmentation and the annotation tool Labelme. Finally, the dataset used is composed of 1930 images, and divided into two subsets with the ratio of 6:4, that is, 1130 images as the training set and 800 pictures as the testing set, which can provide enough training and testing data for the neural network (Zuo et al, 2020). Note that all the testing images were used for the evaluation of our deep learning‐based method and they were not included in the training dataset.…”

Section: Experimental Model Testsmentioning

confidence: 99%

A deep learning technique based flood propagation experiment

Hou

Bai

et al. 2021

J Flood Risk Management

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This work presents an experiment involving detailed fluvial flood propagation process. Comparing to the existing flood experiments which collect hydrodynamical data just at gauges, flood evolution process in river channel and flood plain is measured and temporal–spatial data are provided. In the experiment, three inflow patterns are considered to reflect the different severity of the floods. The flood propagation and inundation are captured by using an array of surveillance cameras. The images are pre‐processed by applying camera calibration method to correct the barrel distortion. A deep learning technique is introduced to automatically identify inundated area. The inundation process is therefore obtained by identifying image series. In addition to the spatial data, the water level evolutions at three gauges are also monitored to supply detailed hydrodynamic information at gauges. The repeatability of the experiments and reliability of the deep learning technique are verified. The experimental data including spatial and point hydrodynamic features for flood events can be used to systematically validate numerical model and calibrate parameters.

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“…However, this method is not robust enough for complex environments, such as various backgrounds and lighting. Zuo et al [ 15 ] improved the existing Mask-RCNN approach [ 16 ], classifying the type of pointer meters while predicting the pointer binary mask and then calculating the readings of a pointer table according to the angle of the pointer. However, this method is designed for a specific environment, which reduces the method’s application scope in a real-world environment.…”

Section: Related Workmentioning

confidence: 99%

Image-Based Automatic Watermeter Reading under Challenging Environments

Hong

Yiwei

Lin

et al. 2021

Sensors

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With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.

show abstract

A robust approach to reading recognition of pointer meters based on improved mask-RCNN

Cited by 73 publications

References 18 publications

Towards Image-Based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

Towards Image-Based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

A deep learning technique based flood propagation experiment

Image-Based Automatic Watermeter Reading under Challenging Environments

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