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
YOU MUST NOT choose the same topic of previous labs
Defect detection
Face recognition
Pedestrian/Vehicle/Signpost detection for autonomous driving
Anomaly detection 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
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
Repository example: DLIP_Project_keywords_2022
e.g. DLIP_Project_MaskDetection_2022
Upload the source code and report in your github repository
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
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