Artificial Intelligence (AI) Product Defect Detection Technology and Case Practice

Course Code : 10012103

Location : HKPC Building 78 Tat Chee Avenue Kowloon

Commencement Date: 2021-11-14

Enquiry : Miss Hui | +852 2788 5787 | yukihui@hkpc.org; Mr. Liang | +852 2788 5305 | hobertliang@hkpc.org

This course is approved by the “Re-industrialization and Technology Training Program” accreditation. Eligible students can receive up to 2/3 tuition subsidy. For details, please visit: https://rttp.vtc.edu.hk。

This course will allow students to understand the types of common product defect imaging tasks and the overall concept of how deep learning is applied to product defect image recognition; this technology can be applied to the detection of abnormal production of auto parts, aviation parts, metal molds, watches and other electronic parts , So that students understand the key points that must be paid attention to when applying and developing related systems:

Introduce the principle of convolutional neural network and how to use convolutional neural network (CNN) for the task of product defect image classification.
Introduce object detection models and how to use related models for product defect detection.

Course purpose

Understand the basic characteristics of images and the types of image tasks for common product defects.
Familiar with the principles of deep learning, and learn to use deep learning to implement image data projects, image classification and object detection.
Understand the principles and applications of well-known deep learning models in the field of contemporary computer vision
Learn to use Python, NumPy, Matplotlib, and Pandas.
Familiar with deep learning related library tools, and have the ability to implement projects.

The course will be given free “Project Manager practical training course” (non-live broadcast course) (approximately 25.3 hours in total)

Course overview

Date	Contents
November 14 (Sun)	Course 1: Introduction to Computer Vision, Basic Image Operation 1. Introduction to Python Numpy package Introduction to Numpy Numpy implementation 2. Introduction to Python Pandas package Introduction to Pandas Pandas implementation 3. Introduction to Matplotlib Suite Introduction to Matplotlib Matplotlib implementation 4. Introduction to Computer Vision and OpenCV Introduction to Computer Vision Image basic operation
November 20 (Sat)	Course 2: Deep Learning Fundamentals and Practice 1. Introduction to the basics of deep learning Basic concepts of deep learning Advantages of deep learning Supervised and unsupervised learning Course 3: Convolutional Neural Network Identification Practice 2. Introduction to DNN Neural Network Build DNN neural network model Define loss function Optimize the neural network [Example] Implement photo classifier
November 21 (Sun)	Course 4: Advanced Image Recognition and Analysis Practice 1. Theory and Practice of Segmentation Model Defect image cutting analysis with semantic cutting Practical application of transfer learning with classic CNN neural network model CNN object cutting Introduction to the architecture of mask-RCNN and other object cutting models [Example] Implementing the classification of photos of surface scratches and defects 2. Object detection technology theory and implementation Yolo principle introduction [Example] Implementation of defective photo object detection [Example] Use of annotation tool
December 5 (Sun)	3. CNN image segmentation Introduction to U-Net principle [Example] Implement image segmentation 4. CNN attitude detection Introduction to the principle of attitude detection [Example] Implement the posture detection of people in the factory 5. Anomaly detection Introduction to anomaly detection principle [Example] Implement anomaly detection in factory data set 6. Practical experience sharing Practical problems Real case sharing

– Students who complete the course will be awarded a certificate of attendance by the Hong Kong Productivity Council

Date and Time

November 14 (Sun), November 20 (Sat), November 21 (Sun), and December 5 (Sun), 2021
9:30 am to 12:30 pm, 1:30 pm to 4:30 pm (4 days, 6 hours per day, 24 hours in total)

Language

Mandarin supplemented with English terminology (Chinese and English handouts)

Lecturer

Qiu Youwei Chief Executive Officer of Taiwan Large Number Software Co., Ltd. (LargitData)

Served as an engineer at Trend Micro Taiwan, a professional lecturer at the International Talent Development Center of the Taiwan Association for Information Technology, and a data public opinion analyst. He is also an entrepreneur and data scientist dedicated to providing Data-as-a-Service. He has considerable expertise in big data analysis. Rich practical experience. At present, it specializes in providing public opinion analysis services to various enterprises and industries, and its customers span industries such as semiconductors, smart manufacturing, networking, telecommunications, and government agencies.
Qiu Youwei has won the AngelHacks Taiwan First Prize, the International Hackathon-Invincible Hacker Award, the winner of the China Cloud Computing Big Data Innovation Project Selection, and the Merit Award of the Cross-Strait Collaborative Innovation Roadshow Competition.

Xie Zhijie, International Project Management Institute (PMI), East Asia mentor

Mr. Xie Zhijie graduated from Stanford University with the aerospace master’s program. In the past, he led Sony Ericcson’s global product development team. He has more than 10 years of rich qualifications in medium and large-scale project planning, execution, management, and consulting, focusing on innovative products , Cross-field and cross-cultural project environment, invited to serve as training lecturers for dozens of well-known enterprises, government agencies, and academic units at home and abroad.

Course fees

HK$2,400 after subsidy (original price of HK$7,200, up to HK$4,800 for RTTP subsidy)

Target student(s)

Engineers and managers engaged in auto parts, aviation, metal molds, clocks, and other electronic parts related industries

Course pamphlet