Lucas Sena

Mobile devices have revolutionized image acquisition, enabling diverse communication and image-processing applications. One promising application is automated damage detection in printed circuit boards (PCBs), crucial for quality control in electronics manufacturing. However, unlike controlled environments, mobile device image acquisition introduces challenges such as non-uniform lighting, background interference, and varying camera resolution, which can affect the accuracy of deep learning models. This paper presents a case study investigating the impact of domain-specific, no-reference image quality metrics on the performance of deep-learning object detection models for PCB damage detection. We evaluate nine metrics, including five novel contributions, using a semantic segmentation approach to measure foreground and background quality. Our study assesses how these metrics influence the performance of deep neural network architectures, determining optimal thresholds that separate high and low-quality images. Experiments are conducted on real-life images captured with smartphones in industrial settings. Our findings indicate that filtering poor-quality images based on these metrics could significantly improve detection performance, offering practical benefits for mobile damage detection applications.

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.

Lucas Cabral , Victor Farias , Lucas Sena , Iago Chaves , João Paulo Pordeus , João Pedro Santiago , Diego Sá , Javam Machado ,& João Paulo Madeiro Identifying Customer Induced Damage (CID) is a key part in Read More ...