UTeM navigation system: pedestrian and traffic sign detection using CNN algorithm - Wan Bejuri, Wan Mohd Ya'akob; Harun, Muhammad Harraz; Mohamad, Abdul Karim (Little Lion Scientific, 2022)
Navigation is a common problem for all drivers, especially university visitors. Unfamiliar place making the driver become careless and unaware, which give hazard to pedestrians and driver itself. Thus, this system aims to solve the problems, by developing mobile navigation with safety features by taking UTeM campus as our scope of the study. The system using algorithm using CNN as an algorithm and the architecture used is Tiny-YOLOv2 to detect traffic signs and pedestrians. To begin, the dataset containing Person and Traffic Sign images and their annotations will first need to be acquired. Then, the CNN model will be trained and tested. As a result, our proposed system shows that the mean average precision for both classes can achieve as 90.44%, when it is implemented in a conventional smartphone. This is proof that our system can provide better capability when it is implemented with a smartphone device. Thus, it contributes to being a new mobile navigation system that can provide multiple capabilities, instead of navigation functions. In conclusion, our system was proven to be a valuable solution for the mobile navigation system. In addition, it is implicated to educate the driver community to be a responsible and alert drivers.