Tiny Machine Learning for High Accuracy Product Quality Inspection

The quality inspection of industrial products is a fundamental step in large-scale production as it boosts the yield and reduces 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 the limited memory and energy resources. This paper presents an innovative sensor system with three 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 the tinyML reducing the 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 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 FPS and 2 FPS.