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Top Electronics Datasets

Use cases for computer and machine vision in electronics manufacturing include defect detection, PCB board and semiconductor component recognition, verifying electronic schematics on pictorial circuit diagrams, and more.

Resistor Detection

Overview

The purpose of the resistor detection dataset is to provide training and test data for an object detection model able to classify simple circuit components on a breadboard.

The end vision is a mobile application where the user takes a picture of their breadboard and the circuit is then automatically simulated. The results of the simulation will inform the user as to whether the circuit constructed is safe, fully connected, and devoid of shorts.

PCB
A project overview
                            		Creating a sorting dataset to differentiate PCB from other objects
                            Descriptions of each class type
                            		PCB - PCB or PCBlike objects.
                            		NonPCB - Anything else
                            		Hands - Human hands that touch PCB
                            Current status and timeline
                            		Update 1 - The first chunk of Dataset is complete. I am excited to explore the possibilities of RoboFlow
                            Links to external resources
                            Contribution and labeling guidelines
                            

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.

This dataset was originally created by Vanitchaporn. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/vanitchaporn/circuit-gexit.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Anonymous. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/new-workspace-rt1da/solarpaneldetectmodel.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

PCB Defect Detection

classes

  • 0 missing hole
  • 1 mouse bite
  • 2 open circuit
  • 3 short
  • 4 spur
  • 5 spurious copper

Dataset usato per addestrare il modello Detectron2 nel porgetto delle schede