- Research engineer/ Research and development of computer vision solutions and algorithms for IoT applications at Bosch CR/RIX-AP
- Senior system engineer / Developing computer vision related topics for surround view system in Bosch CM
- Vision solution engineer / Designing machine vision solutions and computer vision algorithms for consumer electronics industry in COGNEX
- 14 patents for computer vision related topics from 2018
Yulong Yan, Zhuo Zou, Hui Xie, Yu Gao, Lirong Zheng (2020)
- IEEE Internet of Things Journal
Interview with Gao Yu
Research and development of computer vision solutions and algorithms for IoT applications
Please tell us what fascinates you most about research.
The most fascinating thing for computer vision is the boosting of computer vision technology, especially with the rise of deep learning. Many problems such as facial recognition, object detection only had limited performance under several controlled experimental data sets 10 years ago before I started working in this field. Now the performance for the computer vision algorithms for these topics is already applied in our daily lives.
What makes research done at Bosch so special?
The diversity in Bosch Research, in particular CR/RIX-AP. really widens my horizons in the field of computer vision since RIX-AP have several research fields all related to computer vision. We have projects and research topics related to IoT, autonomous driving and robotics and my computer vision background can really be applied to various areas and solve different problems. For example, although most of my work is focused on IoT computer vision topics, I can also contribute to the local robotic research topics here since I have a lot of experience related to the machine vision industry. I can also contribute to the autonomous driving topics here since I have worked with vision topics related to car multimedia before.
What research topics are you currently working on at Bosch?
The current topic I'm working on is using computer vision methods to generate a unique digital ID for industrial parts such as injectors and consumer goods such as luxury bags.
What are the biggest scientific challenges in your field of research?
The topic of IoT gives us a chance to directly face potential customers. This also means our research related topics directly face challenges from the end user. For example, the biggest challenges for image-based digital object ID is how to keep the balance between accuracy and the user’s experience since the appurtenance of an object varies under different viewing angles and lighting conditions but the queries from the end user usually only contain one image taken by mobile phone from a random view and lighting condition.
How do the results of your research become part of solutions "Invented for life"?
To make the research results easy to use and easy to access for the customer, the results of my research become part of the "invented for life" solutions. Thanks to the team, we scaled down our vision solution to fit the 'WeChat mini program' (most popular social network platform in China). As a result, the customer can experience the capabilities of our solution without installing another APP. We are also exploring the possibilities to run machine learning based computer vision algorithms on 'WeChat mini program' to offer more a user-friendly approach for our solution.