smdComponents Image Dataset

This project was created for research work by

Dainius Varna and Vytautas Abromavičius of Vilnius Gediminias Technical University in Lithuania.

The dataset consists of images of SMD-type electronic components, which are moving on a conveyor belt. There are four types of components in the collected dataset:

  1. Capacitors
  2. Resistors
  3. Diodes
  4. Transistors

This is the initial dataset that was augmented to create the final model. Download the raw-images dataset version (v2) of this project to start your own custom project.

Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), 03227 Vilnius, Lithuania; vgtu@vgtu.lt

Dataset Collection

The dataset was collected using Nvidia Data Capture Control.

Figure 3 from the Paper - Example Image of each Component Type
Figure 3. Four types of electronic components used for the dataset. (a) capacitor, (b) resistor, (c) diode, (d) transistor.

Abstract:

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.

Maintainer

dainius

Last Updated

a month ago

Project Type

Object Detection

Subject

electronic-components

Classes

Condensator, Diode, Resistor, Transistor

License

Public Domain