Educational institutions bridge the academic-industrial practice gap

Al Musannah: Higher education institutions in the Sultanate of Oman have successfully developed an integrated strategic vision aimed at bridging the gap between academic theory and industrial practice.

This vision is being realised through several practical tracks, including linking education directly to industry, solving real-world challenges, and creating integrated training programmes that develop specialised skills tailored to immediate industrial needs.

Graduation projects in universities and colleges have emerged as one of the most effective methods for connecting Artificial Intelligence (AI) with industrial and practical applications.

These projects aim to apply acquired knowledge to solve tangible problems and have the potential to transform into commercial opportunities, especially with the growing national focus on innovation and digital solutions.

These academic endeavors span various fields, particularly AI, where students are equipped with the technical skills necessary for the labor market by turning theoretical ideas into applicable tools for various sectors of life.

At the University of Technology and Applied Sciences (UTAS) in Al Musannah, South Al Batinah Governorate, several standout projects have demonstrated the successful integration of AI into industrial processes. One such project is “Detecting Traffic Violations on University Campuses,” developed by students Khalsa Salem Al Maqbali, Sara Khalid Al Amri, and Nusaiba Suleiman Al Maqbali. This system utilizes advanced AI technologies, including YOLOv8, OCR (Optical Character Recognition), and OpenCV-DNN.

The project was developed as a prototype using the NVIDIA Jetson Nano platform, demonstrating high efficiency and impressive real-time processing capabilities.

By using cameras to monitor parking lots and roads, the system automatically counts vehicles and verifies if they are parked correctly.

 When a violation occurs—such as illegal parking or speeding—the system detects the incident and extracts the license plate information to issue an automatic fine.

This innovative approach was recently recognised and presented at an international conference organised by the Institute of Electrical and Electronics Engineers (IEEE).