PCBsmdComponents Computer Vision Project


7661 images
Explore Dataset

This project was created for research work by

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

This dataset is an augmented version of the dataset.

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. 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

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.


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.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

                            title = { PCBsmdComponents Dataset },
                            type = { Open Source Dataset },
                            author = { Dainius },
                            howpublished = { \url{ https://universe.roboflow.com/dainius/pcbsmdcomponents } },
                            url = { https://universe.roboflow.com/dainius/pcbsmdcomponents },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jun },
                            note = { visited on 2024-07-13 },

Connect Your Model With Program Logic

Find utilities and guides to help you start using the PCBsmdComponents project in your project.



Last Updated

2 years ago

Project Type

Object Detection



Views: 775

Views in previous 30 days: 26

Downloads: 28

Downloads in previous 30 days: 1


Public Domain


Condensator Diode Resistor Transistor