Ai Engineer Image Processing
Resume:
PROJECT
DUONG VU HUNG
SUMMARY
Personable Computer Vision Engineer with 1+ years of experience in one of the pioneer companies in the field of vision. A versatile, hardworking individual; driven to meet or exceed a company’s expectations to deliver high-quality vision software products. Experienced in Python, AI, Computer Vision, Image Processing, lighting and camera systems.
An effective listener that can communicate technical information in an easily understandable way. Offers a strong background in creative problem-solving and a proven ability to multi-task and prioritize in fast- paced, stressful environments.
https://github.com/vuhungtvt2018
VSTECH Thu Duc City, Ho Chi Minh City
AI Engineer June. 2022 - Present
Built a plan. Selected and recommend cameras and lights. Researched the layout of cameras, lights and products. Select and recommend computers(cpu, gpu card, screen ) for the project. Researched and developed vision software(included GUI). Researched methods to solve customers' problems such as applying image processing methods, learning and deep learning models.
Learn new models, deep learning techniques, in-depth data processing methods, in-depth image processing methods. Interact and provide technical customer support.
RESEACH AND DEVELOP IDENTIFY DEFECTS ON THE PRODUCT SURFACE MODEL.
- Includes 2 method:
OPTICAL CHARACTER VERIFICATION SOFTWARE
- Built a model to recognize characters on product surfaces for Fumakilla Vietnam Pte. Built and develop OCV software
- Technologies used: Yolov7, K-means, Feature Extracture using ResNet50, Image Processing, Etc.
- Team included 3 people: 1 leader, 1 hardware supporter, 1 developer DRAEM: application of DRAEM method (semi-supervised learning) to detect defects on the surface. DEFECT SEGMENTATION: combination of SinGan model and Segmentation model. SinGan model was used create fake defect image, based on real defect image. Dataset was used for training at Segmentation model..
- Technologies used: Draem model, Image Processing, GAN model, Segmentation.
- Team included 2 people: 1 leader, 1 developer (researcher) RESEARCH MODELS FOR OBJECT DETECTION AND CLASSIFICATION USING EDGE LEARNING
- In process
- Team included 2 people: 1 leader, 1 developer (researcher) February 14, 2000
District 9, HCM City
Computer Vision Engineer
CAREER OBJECTIVE
Contribute your professional knowledge and experience to the company's development. Become a professional AI Engineer in the next 3-4 years and an AI Leader in the next 5-6 years. Learn new knowledge, models, methods from research articles in the field of AI and Image Processing PROFESSIONAL EXPERIENCE
**************@*****.***
www.linkedin.com/in/vuhung2016
Frameworks: TensorFlow, Keras, Scikit-learn, Pytorch, PyQt5, Qt Designer Object Detection, OCR, OCV, Classification, Image Segmentation, Object Tracking Exploratory Data Analysis, Data Processing, Data Pretrain model, Ensembling, Holdout Dataset, . . . Traditional Image Processing
Communication and Listening, Teamworking, Self- study, Writing reports and proposals Problem-solving skills, Learning new technologies
EDUCATION
August. 2018 - April. 2023 HO CHI MINH UNIVERSITY OF TECHNOLOGY Automation and Control Engineering GPA: 7.76/10. April. 2023 - March. 2024 AI VIETNAM
Data science and Artificial intelligence (AIO 2023) HARD SKILLS
SOFT SKILLS
VISION BASED STATE RECOGNITION OF 220KV AND 110KV DISCONNECTORS SWITCHES IN POWER SUBSTATIONS USING YOLOV5
- Collected data, used transfer learning on yolov5, evaluated and deploy model on software
- Technologies used:Yolov5
- Team included 4 people: 1 leader, 3 developer
FINETUNE FACENET MODEL USING PYTORCH. TRAIN, EVALUATE AND IMPROVE THE ACCURACY OF FACENET MODEL WITH TRIPLET LOSS, SOFTMAX
- Collected data, fine tune facial model using both Tensorflow and Pytorch
- Technologies used: facial-recognition, Yunet
- Team included 2 people: 1 leader, 1 developer (researcher)
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