Portfolio

Some projects I have worked on.

Description: An advanced driver-assistance system on Jetson Nano embedded computer with four main functions: forward collision warning, lane departure warning, traffic sign recognition and overspeed warning. I trained and optimized three deep neural networks with TensorRT technology for these functions. I also implement a highly efficient pipeline for the system and a touch user interface in C++ Qt.
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:

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/

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

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

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

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.

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.

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 (Vietnameses): Thiết kế thùng rác thông minh tự phân loại rác với Raspberry Pi 3.

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

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