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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>Deep Learning AOI | Projects</title>
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<h1><a href="https://goheesheng.github.io/">Goh Ee Sheng</a></h1>
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<h2><a href="index.html#projects">Back</a></h2>
<p>Deep Learning AOI Model</p>
</div>
<div class="container">
<div class="container-fluid">
<div id="aoiIndicators" class="carousel slide" data-ride="carousel">
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<div class="row container project-showcase" style="background:none;">
<div class="col-lg-7">
<h4>Project Description:</h4>
<p>For this project, I was tasked to create a Deep Learning Model to detect automatically inspect electronic components on circuit board. This project is to be displayed and represent Fraunhofer Singapore in the coming trade show.</p>
<p> I have chosen to use CVAT to annotate and preprocess the image data in COCO json format. Additionally, using Detectron2, Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms to create the Deep Learning Model.</p>
<h6>This work includes:</h6>
<ul>
<li><i class="icofont-rounded-right"></i>Learning about the theory of CNNs for object detection</li>
<li><i class="icofont-rounded-right"></i>Data collection & extension of an existing data set with a semi-automatic labelling tool</li>
<li><i class="icofont-rounded-right"></i>Training of a CNN model with the extended data</li>
<li><i class="icofont-rounded-right"></i>Evaluation of model performance (Accuracy, Precision, Recall etc. ) and comparing it to previous models</li>
<li><i class="icofont-rounded-right"></i>Development of an additional classification module to analyse the connectivity of the electric components</li>
</ul>
<div class="mt-4">
<h4>My Contributions:</h4>
<ul>
<li><i class="icofont-rounded-right"></i>Collect Data</li>
<li><i class="icofont-rounded-right"></i>Annotate & Label data</li>
<li><i class="icofont-rounded-right"></i>Extract & Combine data with colleagues</li>
<li><i class="icofont-rounded-right"></i>Split data into train & test dataset with [8:2]</li>
<li><i class="icofont-rounded-right"></i>Train the model using extracted data</li>
</ul>
</div>
</div>
<div class="col-lg-1">
</div>
<div class="col-lg-4">
<h5>Github Repositories:</h5>
<ul>
<li><i class="icofont-rounded-right"></i><a href="https://github.com/goheesheng/AOI_FHG" target="_blank">https://github.com/goheesheng/AOI_FHG</a></li>
</ul>
<h5>Created Using:</h5>
<ul>
<li><i class="icofont-rounded-right"></i>CV2</li>
<li><i class="icofont-rounded-right"></i>Cuda</li>
<li><i class="icofont-rounded-right"></i>Docker</li>
<li><i class="icofont-rounded-right"></i>Detectron2</li>
<li><i class="icofont-rounded-right"></i>Iriun</li>
<li><i class="icofont-rounded-right"></i>Python | PyTorch</li>
</ul>
<h5 style="margin-bottom: 10px;">Professor Marius Erdt, Deputy Director of Fraunhofer</h5>
<a style="margin-bottom: 20px;" class="btn btn-info" href="https://docs.google.com/spreadsheets/d/1qElhqvNelSbh-rUbFMlc0jegYttL-lew/edit?usp=sharing&ouid=116419561461505307043&rtpof=true&sd=true" target="_blank">Testimonial</a>
<br>
<a class="btn btn-success" href="https://docs.google.com/presentation/d/1GqtJGDyyUjwBnGqNRj5jXBhxOO6r1fgi/edit?usp=sharing&ouid=116419561461505307043&rtpof=true&sd=true" target="_blank">Presentation Slides</a>
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Goh Ee Sheng <span style="color:#18d26e;">©</span> 2023</a>
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