Deep Learning–based Number Detection and Recognition for Gas Meter Reading

Son, Changeui; Park, Seokmok; Lee, Jae-Min; Paik, Joonki

doi:10.5573/ieiespc.2019.8.5.367

Cited by 14 publications

(9 citation statements)

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“…Recently, some studies based on computer vision had been proposed to recognize digits [20,21]. Some deep learning results were only focused on analog meters to perform digit number recognition but not on PLC screens [22], and even a large R-CNN network was used to train the recognition model [23]. It really needs high computing power like GPU to train the object detection and recognition models.…”

Section: Introductionmentioning

confidence: 99%

The Vision-Based Data Reader in IoT System for Smart Factory

2022

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The proposed research is based on a real plastic injection factory for cutting board production. Most existing approaches for smart manufacturing tried to build the total solution of IoT by moving forward to the standard of industry 4.0. Under the cost considerations, this will not be acceptable to most factories, so we proposed the vision based technology to solve their immediate problem. Real-time machine condition monitoring is important for making great products and measuring line productivity or factory productivity. The study focused on a vision-based data reader (VDR) in edge computing for smart factories. A simple camera embedded in Field Programmable Gate Array (FPGA) was attached to monitor the screen on the control panel of the machines. Each end device was preprogrammed to capture images and process data on its own. The preprocessing step was then performed to have the normalized illumination of the captured image. A saliency map was generated to detect the required region for recognition. Finally, digit recognition was performed and the recognized digits were sent to the IoT system. The most significant contribution of the proposed VDR system used the compact deep learning model for training and testing purposes to fit the requirement of cost consideration and real-time monitoring in edge computing. To build the compact model, different convolution filters were tested to fit the performance requirement. Experimentations on a real plastic cutting board factory showed the improvement in manufacturing products by the proposed system and achieved a high digit recognition accuracy of 97.56%. In addition, the prototype system had low power and low latency advantages.

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Section: Introductionmentioning

confidence: 99%

The Vision-Based Data Reader in IoT System for Smart Factory

2022

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“…As far as we are concerned, object detection plays an important role in vision-based applications such as face detection, traffic sign detection, character/digit detection, person re-identification, animal detection, and meter reading [5,[19][20][21][22]. So far, in addition to traditional machine-learning-based methods, there are some CNN-based object-detection algorithms, including R-CNN (region-based convolutional neural network), Faster R-CNN, you only look once (YOLO), and a single-shot multiBox detector (SSD), RetinaNet et al [23].…”

Section: Introductionmentioning

confidence: 99%

“…So far, in addition to traditional machine-learning-based methods, there are some CNN-based object-detection algorithms, including R-CNN (region-based convolutional neural network), Faster R-CNN, you only look once (YOLO), and a single-shot multiBox detector (SSD), RetinaNet et al [23]. For example, an automatic meter-reading method was proposed for gas meter reading [20]. The proposed method [20] is composed of three steps: meter detection, digit segmentation, and number recognition.…”

Section: Introductionmentioning

confidence: 99%

“…For example, an automatic meter-reading method was proposed for gas meter reading [20]. The proposed method [20] is composed of three steps: meter detection, digit segmentation, and number recognition. In the three steps, YOLOv3, maximally stable extremal regions (MSER), and a modified VGG network are adopted in [20].…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method [20] is composed of three steps: meter detection, digit segmentation, and number recognition. In the three steps, YOLOv3, maximally stable extremal regions (MSER), and a modified VGG network are adopted in [20]. In [22], a two-stage method is proposed for an automatic meter reading.…”

Section: Introductionmentioning

confidence: 99%

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Keyword Detection Based on RetinaNet and Transfer Learning for Personal Information Protection in Document Images

Lin

2021

Applied Sciences

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In this paper, a keyword detection scheme is proposed based on deep convolutional neural networks for personal information protection in document images. The proposed scheme is composed of key character detection and lexicon analysis. The first part is the key character detection developed based on RetinaNet and transfer learning. To find the key characters, RetinaNet, which is composed of convolutional layers featuring a pyramid network and two subnets, is exploited to detect key characters within the region of interest in a document image. After the key character detection, the second part is a lexicon analysis, which analyzes and combines several key characters to find the keywords. To train the model of RetinaNet, synthetic image generation and data augmentation are exploited to yield a large image dataset. To evaluate the proposed scheme, many document images are selected for testing, and two performance measurements, IoU (Intersection Over Union) and mAP (Mean Average Precision), are used in this paper. Experimental results show that the mAP rates of the proposed scheme are 85.1% and 85.84% for key character detection and keyword detection, respectively. Furthermore, the proposed scheme is superior to Tesseract OCR (Optical Character Recognition) software for detecting the key characters in document images. The experimental results demonstrate that the proposed method can effectively localize and recognize these keywords within noisy document images with Mandarin Chinese words.

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