A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt

Applied Sciences

Dainius Varna¹  Vytautas Abromavičius¹ 

[1] Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), 03227 Vilnius, Lithuania;

关键词: convolutional neural networks;    deep learning;    machine learning;    object detection;    SMD component;   

DOI  :  10.3390/app12115608

来源: DOAJ

【 摘 要 】

The presented research addresses the real-time object detection problem with small and moving objects, specifically the surface-mount component on a conveyor. Detecting and counting small moving objects on the assembly line is a challenge. In order to meet the requirements of real-time applications, state-of-the-art electronic component detection and classification algorithms are implemented into powerful hardware systems. This work proposes a low-cost system with an embedded microcomputer to detect surface-mount components on a conveyor belt in real time. The system detects moving, packed, and unpacked surface-mount components. The system’s performance was experimentally investigated by implementing several object-detection algorithms. The system’s performance with different algorithm implementations was compared using mean average precision and inference time. The results of four different surface-mount components showed average precision scores of 97.3% and 97.7% for capacitor and resistor detection. The findings suggest that the system with the implemented YOLOv4-tiny algorithm on the Jetson Nano 4 GB microcomputer achieves a mean average precision score of 88.03% with an inference time of 56.4 ms and 87.98% mean average precision with 11.2 ms inference time on the Tesla P100 16 GB platform.

【 授权许可】

Unknown