On September 20, the 10th National Computer Course Experimental Teaching Case Competition announced its results. The case titled "Design and Implementation of a Target Detection Algorithm Based on a Quadruped Robot," submitted by faculty members Li Na and Ren Kai from the SCMU School of Computer Science (School of Artificial Intelligence), was awarded the First Prize.

Receiving the Award Certificate. Picture Source: SCMU School of Computer Science (School of Artificial Intelligence)

Faculty members Li Na and Ren Kai have long been responsible for teaching the core course "Deep Learning" in the Artificial Intelligence program. They have meticulously designed the experimental teaching components of this course, guiding students to deeply understand the strengths and weaknesses of various algorithms and their performance in real-world scenarios, thereby transforming theoretical knowledge into the ability to solve practical problems. The award-winning case involves deploying a deep learning model on a quadruped robot (robotic dog), requiring comprehensive consideration of factors such as environmental lighting changes, motion blur, limited computational resources, and hardware-software co-design. While pursuing model accuracy, the case also holistically enhances the robot's speed, power consumption, and stability, thereby cultivating students' capabilities to tackle complex engineering challenges.

Students Debugging the Algorithm Model, On-site Experimental Assessment, Picture Source: SCMU School of Computer Science (School of Artificial Intelligence)

The National Computer Course Experimental Teaching Case Competition is organized by the Computer Discipline Group of the National Demonstration Center for Experimental Education and the Steering Committee for Computer Science Education in Higher Education Institutions under the Ministry of Education. It aims to align the experimental teaching systems and content of computer-related courses across universities, promote inquiry-based and innovative experimental teaching reforms, foster problem-solving skills, enhance students' innovation awareness and engineering practice abilities, and facilitate the widespread application and sharing of advanced teaching philosophies, content, technologies, and methods in the process of higher education quality engineering, ultimately improving the experimental teaching standards of university faculty.