# LAB: CNN Object Detection 2

## LAB: CNN Object Detection (Free Topic)

## I. Introduction

In this lab, you are required to create a simple application that uses a CNN-based object detection. You are free to choose the application topic of your interest.

### Application Examples

Choose a topic relevant to Mechatronics. [Refer to past lab reports on object detection](https://ykkim.gitbook.io/dlip/dlip-project/dlip-projects)

> YOU MUST NOT choose the same topic of previous labs

* [Kaggle Data Challenge](https://www.kaggle.com/competitions)
* Defect detection
* Face recognition
* Pedestrian/Vehicle/Signpost detection for autonomous driving
* [People/vehicle Counting](https://www.youtube.com/watch?v=19vaot75JCY)
* [Anomaly detection](https://towardsdatascience.com/anomaly-detection-in-images-777534980aeb) and more

### Guidelines

The whole code should be programmed using OpenCV-Python and Pytorch.

* DO NOT copy a project from online sites.
* You can refer to any online material and github repository for assistance and getting ideas with proper reference citation.
* You can use any pretrained object detection model, such as YOLO v3\~v5, etc..
* You may also train the model using custom datasets
* You can clone a github repository of the object detection model(e.g. YOLOv5), as long as you cite it in the reference.

If you create a simple hardware for demonstration, you will get extra score. Please ask TA if you need an embedded GPU board or any other hardware materials.

## II. Report and Demo Video

This lab will be scored depending on the Contents, Complexity, and Completeness .

You are required to write a concise report and submit the program files and the demo video.

### Report

The lab report must be written as a 'Tutorial' format to explain the whole process A to Z in detail. Your report will be posted in the class website and be open to public.

A high score will be given if a reader should be able to follow the report instructions and get the same results.

Use the report template given here: <https://ykkim.gitbook.io/dlip/dlip-project/report-template>

* Also, see example tutorials: [example 1](https://keras.io/examples/vision/retinanet/), [example 2](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/classification.ipynb)

**Requirement**

* Write the report in markdown ‘\*.md’ format
* You need to include concise explanations and codes for each process in the report
* Create a Repository(Public) in Your Github&#x20;
  * Repository example: DLIP\_Project\_keywords\_2022
  * e.g.  DLIP\_Project\_MaskDetection\_2022
* Upload the source code and report in your github repository&#x20;
* Then, download your repository as zip file and submit the zip file to online.handong.edu. The Zip file includes
  * Report (\*.md)
  * Report (\*.pdf)
  * src (source code)
  * images (sample, test image files )
  * data (whole dataset or download link)

### Demo Video

You must create a demo video (\~40 sec) that will be uploaded in the Lecture's Youtube channel.

* The demo video must contain
  * Title page: course name (DLIP2022-1 by Y.-K.Kim), your name, your project tile, date
  * Introduction, Dataset, Model, Results, Conclusion
* Embed the youtube video in your report
* Submit the video file to LMS or TA's email