> For the complete documentation index, see [llms.txt](https://ykkim.gitbook.io/dlip/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ykkim.gitbook.io/dlip/deep-learning-for-perception/lab/lab-cnn-object-detection.md).

# 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. Submission

### Files

**`$PROJECT_NAME$` = DLIP\_YEAR\_FinalProject\_KEYWORD\_Name1\_Name2**

* Submit as  Zip File:  `$PROJECT_NAME$` .zip 

* The Zip file includes

  * report (\*.md), (\*.pdf)
  * src (source code)
  * dataset (sample, test image files, or download link)
  * presentation
  * demoVideo

* Example:

<figure><img src="https://3883264845-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MR8tEAjhiC8uN1kHR2J%2Fuploads%2FvLGCriskpEHl3lqTy42b%2Fimage.png?alt=media&amp;token=8a4baae1-c4f2-4a84-badc-631c7369c1fd" alt=""><figcaption></figcaption></figure>

* Report File Name should be  `$PROJECT_NAME$` .pdf

<figure><img src="https://3883264845-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MR8tEAjhiC8uN1kHR2J%2Fuploads%2Fm8bLwZPSITFbLUka2Elv%2Fimage.png?alt=media&amp;token=2ca6525c-ad61-431e-af82-c230f726cc80" alt=""><figcaption></figcaption></figure>

#### Github Repos

* 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
  * Repository Name:  **`$PROJECT_NAME$`**&#x20;
* Upload the source code and report in your github repository

## III. 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)

### Demo Video

You must create a demo video (10\~15 sec)&#x20;

* The demo video must contain
  * **Title page:**
    * course name (DLIP2026-1 by Y.-K.Kim), your name, your project tile, date etc
  * Introduction
  * Results
* Embed the youtube video in your report
* Submit the video file to LMS or TA's email