My Projects

This page introduces some of my projects.

VN AIDR - OPEN SOURCE MEDICAL IMAGE PROCESSING

• Description: VN AIDr project is an open source medical image processing solution for AI learners and hobbyists. The core of this project is a medical image processing platform using client-server architecture. On this platform, we design, train and evaluate machine learning models and algorithms for medical image analysis and medical report autocompletion.
• My role: Leader + Build deep learning models.
• Project Website: https://dr.vnopenai.org/
• Github: https://github.com/VNOpenAI/vn-aidr

PUSHUP COUNTER - DESKTOP APP

• Description: This project employs BlazePose - a SOTA model for real-time human pose detection, a single frame action recognition model, and some signal processing algorithms to count pushups from a video stream or webcam.
• My role: Leader + Build BlazePose model.
• Github: https://github.com/VNOpenAI/pushup-counter-app
• Demo: https://www.youtube.com/watch?v=pm2mGmgwSZo

ADVANCED DRIVER-ASSISTANCE SYSTEM - The NVIDIA Jetson Project Of the Month Winner

• Description: An ADAS system that uses Jetson Nano as the hardware with four main functions: forward collision warning, lane departure warning, traffic sign recognition and overspeed warning. I trained and optimized three deep neural networks to run simultaneously on Jetson Nano (CenterNet-ResNet18 for object detection, U-Net for lane line segmentation and ResNet-18 for traffic sign classification). I won The NVIDIA Jetson Project Of the Month Prize with this project and received a Jetson AGX Xavier Developer Kit.
• Languages: Python, C++.
• Technologies: Tensorflow, Pytorch, TensorRT, object detection (CenterNet), segmentation (U-Net) and classification (ResNet-18)
• Github: https://github.com/vietanhdev/car-smart-cam
• Blog posts:
  1. Project of Month - NVIDIA Developer News.
  2. Advanced driver-assistance system on Jetson Nano Part 1 - Intro & Hardware design
  3. Advanced driver-assistance system on Jetson Nano Part 2 - Software design
  4. Advanced driver-assistance system on Jetson Nano Part 3 - Deep neural networks

SELF-DRIVING CAR 2020 - TOP 1 Hanoi University of Science and Technology - FPT Driverless Car Challenge

• Description: This project helped my group achieving The First Prize in FPT Self-driving Car challenge 2020 - University round at Hanoi University of Science and Technology. In this challenge, we built a ROS node with deep neural networks to control a autonomous car in a simulation environment. My group used E-Net and U-Net for lane segmentation and customized FaceBoxes for a light-weight traffic sign detection networks. I was the leader of this group, worked on the architecture, semantic segmentation model and car controlling.
• Languages: Python.
• Technologies: ROS (Robot Operating System), OpenCV, segmentation models (U-Net, ENet), object detection (FaceBoxes), Docker.
• Github: https://github.com/vietanhdev/autonomous-car-2020
• Blog post: https://aicurious.io/posts/chung-toi-da-xay-dung-xe-tu-hanh-tren-gia-lap-the-nao/

PAPER STREAM - PAPER STREAMING FOR ONLINE EDUCATION - TOP 2 IBM - SoICT Hackathon

• Description: I led a 4-member team to build a paper streaming solution for remote education. Our project got Second Place in SoICT - IBM Hackathon 2020 at HUST. I was responsible for algorithms to capture drawing strokes and handwritten text from a phone camera, filter and create video streaming.
• Languages: Python.
• Technologies: ARUCO, Color-based segmentation, ReactJS, NodeJS, CI-CD.
• Github: https://github.com/vietanhdev/paper_stream
• Blog post: https://aicurious.io/posts/xay-dung-giai-phap-stream-giay-viet-IBM-Hackathon-2020/
• Demo: https://www.youtube.com/watch?v=pmRSfHSfrco

DEEP HEAD POSE 2 - WITH SHUFFLENET V2, EFFICIENT-NET BACKBONES AND FACE LANDMARK ESTIMATION

• Description: A modified Deep Head Pose model by adding new backbones (ShuffleNetV2, EfficientNet), and face landmark estimation.
• Languages: Python.
• Technologies: Deep Head Pose, ShuffleNetV2, EfficientNet, Landmark Estimation.
• Demo: https://youtu.be/SNHnsuNkBkQ
• Github: https://github.com/vietanhdev/deep-head-pose-2

FACECAM - FUNNY CAMERA APP FOR WINDOWS AND UBUNTU

• Description: A camera desktop application with funny face decorations and filters. In this project, I also integrated LBP Cascade and Haar Cascade models trained by myself to detect faces. This project supports both Linux and Windows.
• Languages: C++.
• Technologies: Qt, OpenCV, face detection and alignment algorithms.
• Github: https://github.com/vietanhdev/facecam

SELF-DRIVING CAR 2018 - FPT SELF-DRIVING CAR CHALLENGE

• Description: This is a group project for FPT self-driving car challenge 2018. We used Watershed and Floodfill algorithms for lane segmentation, a color-based method for traffic sign detection and HOG + SVM for sign classification. I worked on the architecture, algorithms for lane segmentation, a method for traffic sign detection and car control algorithm.
• Languages: C++.
• Technologies: OpenCV, Image processing, Color-based Object detection, Traffic Sign detection using HOG SVM.
• Github: https://github.com/vietanhdev/autonomous-car-2018
• Demo: https://www.youtube.com/watch?v=Obv53r7UV34.

ESTOREMANAGER (GROUP PROJECT AT UNIVERSITY)

• Description: We built a completed store management solution with a lot of features such as billing, management UI for bills, warehouse, suppliers, customers, finance and employees. I worked on the desktop application.
• Languages: Desktop App: Javascript, HTML CSS; Server: Java
• Technologies: Desktop App: Electron; Server: Java Spring Boot.
• Github: https://github.com/vietanhdev/e-store-manager
• Demo: Youtube Video.

IOT: SMART TRASH BIN

• Description: My funny IoT project for our environment. This trash bin can classify bottles and other kinds of garbage into different compartments. I finished this project in only 1 day. Most of the time was spent for the mechanical part, sensors, servo. I integrated and run a pre-trained SSD-MobileNet V2 model on a Raspberry Pi computer to detect bottles.
• Technologies: Desktop App: Electron; Server: Java Spring Boot.
• Demo: https://www.youtube.com/watch?v=s1KHGU8nuBM
• Blog post (Vietnamese): Thiết kế thùng rác thông minh tự phân loại rác với Raspberry Pi 3.

BATTLE SHIP (GROUP PROJECT AT UNIVERSITY)

• Description: Online BattleShip game using ReactJS for frontend, Flask for backend and Websocket for realtime communication. We also implemented a chatting system and friend list. I worked mostly on frontend part (ReactJS).
• Github: https://github.com/vietanhdev/online-battleship