Advancing Parking Systems: A Performance Comparison of MobileNet and Canny in License Plate Detection

Rapid advancements in technology, particularly in computer science, have driven progress in image processing, which plays a crucial role in daily life. This research focuses on object recognition through vehicle license plate detection, utilizing an image database to address human errors in recording vehicle numbers that can slow down parking system services. An automated system is proposed to enhance parking management, although challenges in accurately segmenting plates remain. Two segmentation methods are compared: the MobileNet architecture and the Canny algorithm. This study aims to evaluate the segmentation accuracy between the two methods. Canny for its edge detection capabilities that reduce noise, and MobileNet for its effectiveness as a deep learning-based approach. The system is implemented using Python, JavaScript, HTML, and CSS to modernize vehicle license plate segmentation. The results show that MobileNet significantly outperforms the Canny algorithm, achieving a lower Character Error Rate (CER) of 18.8%, compared to Canny's 50.96%, across 13 tested license plate samples. This finding demonstrates that MobileNet offers a more reliable and accurate approach for segmenting vehicle license plates, thereby contributing to the development of a more efficient and automated license plate recognition system.