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.

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

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.

/pdf/the-vision-based-data-reader-in-iot-system-for-smart-factory-2cjso0fu.pdf

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

In the machining system, how to recognize text on the curved metal surface of parts is still a challenging question. In this article, a text spotting framework for curved metal surface based on image rectification and CNN is proposed. First, a curved text rectification method through CNN-based detection and text curve fitting is investigated. The CNN model for text detection is trained on the original images, which is used to obtain the location of characters of the text. The characters are clustered based on location and size, and the text line is fitted based on the centers of characters that belong to the same cluster. The text image is expanded according to the fitting text line, which finishes the rectification of the curve text image. Second, plenty of original text images are rectified by the proposed rectification method to construct the training data set. Third, in the recognition stage, an original text image is rectified and used as the input of the CNN recognition model. In the experiments, after comparing the detection performance between YOLOv3, Faster R-CNN, SSD-ResNet101, and RetinaNet101, RetinaNet101 was chosen as the text detector. Also, the recognition results on the rectification training set and test set generated by the proposed framework are better than those on the original image data sets. The experimental results show that the proposed method can achieve competitive results text spotting for curved metal surface.

Text Spotting for Curved Metal Surface: Clustering, Fitting, and Rectifying

Tire burst of vehicles is one of the main causes of highway accidents. Furthermore, tire aging is the most important cause of tire burst. It will be of great significance to traffic safety, through identifying the tire text code (TTC) to determine whether or not a tire is over the service life. In this article, a coarse-to-fine method of detection and rectification for TTC is proposed. First, text regions are located using the deep convolutional neural network (DCNN) model. Second, through fitting the tire circle curve, the close-up image of TTC is rotated to a positive direction. Third, a text boundary points’ generation strategy is proposed, which is based on the rotating bounding box of each character. Fourth, the curved text is rectified and input to the DCNN recognition model. On the tire image dataset, the detection accuracy of TTC and characters is 94% and 96.5% respectively, and the recognition accuracy is 94.5%. Through additional experimental results, it is proven that the proposed method has strong robustness to illumination and camera shooting angle, which can meet the real-time detection and recognition requirements as well.

Vehicle Tire Text Reader: Text Spotting and Rectifying for Small, Curved, and Rotated Characters

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.

Lightweight CNN based Meter Digit Recognition

The current method of meter reading is manual and error-prone in developing countries. A meter reader logs the reading to calculate the cost of electricity. In recent years, there have been multiple efforts to provide automated solutions to read the meter digits. However, the existing systems extract reading based on a specific meter topology. In this paper, we propose an approach based on Faster R-CNN to extract and recognize digits in an electric meter. We compared our method against several state-of-the-art object detection methods. The proposed approach is robust against different lightening conditions, severe perspective distortions and blurred images. In addition, it is scaleinvariant. Furthermore, we created a new dataset consisting of 10310 images taken from electricity companies in Pakistan to benchmark meter digit recognition task. Experimental results shows the high accuracy of the proposed approach on the created electricity meter dataset.

M. Haroon Yousaf

Papers

Meter Digit Recognition Via Faster R-CNN