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 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.