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Tiny Machine Learning for High Accuracy Product Quality Inspection
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DOI：10.1109/JSEN.2022.3225227.png)
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摘要
En 中文
The quality inspection of industrial products is a fundamental step in large-scale production as it boosts the yield and reduces the costs. Intelligent embedded platforms with built-in tiny machine learning (tinyML) algorithms and cameras can automate quality inspection; however, running complex deep learning algorithms in low-cost and low-power embedded devices is still challenging because of limited memory and energy resources. This article presents an innovative sensor system with three microcontroller unit (MCU)-based tinyML cameras capable of automatic artifact and anomaly detection in plastic components. The system consists of a top camera responsible for identifying shape defects and two side cameras for color anomalies. Data processing is executed locally with tinyML reducing data transmission to a few bytes. Two state-of-the-art convolutional neural network (CNN) architectures are evaluated, namely, MobileNetV2 and SqueezeNet. Results show how both the architectures-with appropriate compression techniques-are suitable to be evaluated by resource-constrained microcontrollers. The networks achieve 99% classification accuracy while maintaining suitable real-time performance, respectively, equal to 5 and 2 frames/s.
Keyword:
Cameras
Inspection
Sensors
Visualization
Cloud computing
Belts
Production
Edge processing
Industrial Internet of Things (I-IoT)
Industry 4.0
tiny machine learning (tinyML)
visual inspection
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
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