Screen Scratch Detection on Credit Card Payment Terminals for Logistics Optimization

Manufacturers of credit and debit card terminals lend their products to retailers, retaining responsibility for repairing or replacing devices upon verification of malfunctions or cosmetic defects. The logistic operator, which processes up to ten thousand products daily, must assess each device's condition through tests and manual visual inspections. Depending on the evaluation, a terminal may be returned to the client or sent to the manufacturer's tech center for repair. This inspection process is highly subjective and prone to human error, as different inspectors may assess the same device inconsistently. To address these inconsistencies, we developed a visual inspection system specifically for evaluating the LCD conditions of payment termi-nals. The hardware includes a 3D-printed structure to optimize image acquisition, an active illumination scheme, and a camera to capture images of the terminals. The desktop application features a YOLOv8 object detection model to highlight defects on the terminal's screen, complemented by a user-friendly interface with various functionalities. Our system improved the efficiency and accuracy of inspections, providing operators with a more reliable tool that ensures consistency. The final model achieved a mean Average Precision (mAP) of 77.4% and a processing speed of 88 milliseconds on CPU. Additionally, the real-time application interface offers low-latency usability, further enhancing the inspection process. After thorough model training and testing conducted in collaboration with the terminal manufacturer, the system has demonstrated its reliability for deployment at the production level.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/sbesc65055.2024.10771821
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
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article

Screen Scratch Detection on Credit Card Payment Terminals for Logistics Optimization

Tiago Vieira, Lucas Silva do Amaral, D. Alves, Hiago L. Cavalcante
Industrial Vision Systems and Defect Detection
article

Screen Scratch Detection on Credit Card Payment Terminals for Logistics Optimization

Tiago Vieira, Lucas Silva do Amaral, D. Alves, Hiago L. Cavalcante
article en

Abstract

Manufacturers of credit and debit card terminals lend their products to retailers, retaining responsibility for repairing or replacing devices upon verification of malfunctions or cosmetic defects. The logistic operator, which processes up to ten thousand products daily, must assess each device's condition through tests and manual visual inspections. Depending on the evaluation, a terminal may be returned to the client or sent to the manufacturer's tech center for repair. This inspection process is highly subjective and prone to human error, as different inspectors may assess the same device inconsistently. To address these inconsistencies, we developed a visual inspection system specifically for evaluating the LCD conditions of payment termi-nals. The hardware includes a 3D-printed structure to optimize image acquisition, an active illumination scheme, and a camera to capture images of the terminals. The desktop application features a YOLOv8 object detection model to highlight defects on the terminal's screen, complemented by a user-friendly interface with various functionalities. Our system improved the efficiency and accuracy of inspections, providing operators with a more reliable tool that ensures consistency. The final model achieved a mean Average Precision (mAP) of 77.4% and a processing speed of 88 milliseconds on CPU. Additionally, the real-time application interface offers low-latency usability, further enhancing the inspection process. After thorough model training and testing conducted in collaboration with the terminal manufacturer, the system has demonstrated its reliability for deployment at the production level.

Universidade Federal de Alagoas (BR)
Decent work and economic growth
Openalex Percentile: Top 69%
Industrial Vision Systems and Defect Detection
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