Research Article
Real-Time Seven Segment Display Detection and Recognition Online System Using CNN
@INPROCEEDINGS{10.1007/978-3-030-57115-3_5, author={Autanan Wannachai and Wanarut Boonyung and Paskorn Champrasert}, title={Real-Time Seven Segment Display Detection and Recognition Online System Using CNN}, proceedings={Bio-inspired Information and Communication Technologies. 12th EAI International Conference, BICT 2020, Shanghai, China, July 7-8, 2020, Proceedings}, proceedings_a={BICT}, year={2020}, month={8}, keywords={Seven-Segment Display Detection Seven-segment recognition Convolution Neural Network Detection Recognition}, doi={10.1007/978-3-030-57115-3_5} }- Autanan Wannachai
Wanarut Boonyung
Paskorn Champrasert
Year: 2020
Real-Time Seven Segment Display Detection and Recognition Online System Using CNN
BICT
Springer
DOI: 10.1007/978-3-030-57115-3_5
Abstract
Typically, manufacturing machines represent their working status via the seven-segment LED display. The operators have to read the machine working status periodically. The process information time-lagging and human-error may occur. These causes may defect the output products and reduce manufacturing productivity. This research paper proposes a real-time and automatic machine display tracking system. The proposed real-time seven-segment LED display recognition system is designed to apply to the actual machines in the manufacturing. However, the camera installation problem degrades the image qualities such as machine vibration, light reflection, brightness, and camera view’s frame changes. The proposedReal-timeSevens segmentDisplay detection and recognition online system usingCNN (RSDC) consists of the camera controller module and theInterpretation ofSeven-Segment display (ISS) framework. The RSDC can track the machine’s display and interpret the camera images to numerical data using the machine learning technique to handle the installation problems. The experiment result shows that the proposed ISS framework has an interpretation accuracy of 91.1%.
