Real-Time Detection of Customer-Induced Damage in Printed Circuit Boards Using Mobile Devices and YOLO Detectors

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Abstract
The identification of Consumer-induced damage is essential for electronics manufacturers’ warranty programs. Consumer Induced Damage (CID) is any damage caused by an unauthorized person, including the consumer. The product warranty does not cover these damages, avoiding expenses in the manufacturer’s revenue. The consumer-induced damage warranty process is usually done manually by technically trained people. However, this task demands a lot of attention to detail, can be time-consuming, and is susceptible to human errors. With this in mind, this work presents an object detection model for low-computational-cost devices that uses computer vision and deep learning methods with YOLO detectors embedded in mobile devices to identify consumer-induced damages on printed circuit boards (PCB). We conducted sixteen experiments with four YOLO neural network architectures and successfully developed a mobile application for CID detection. Our best model achieved a mAP@0.5 of 33.1% and an average of 5.7 FPS on real mobile devices.