Parking Slot Detection and Vacancy Check Based on Deep Learning Method

Abstract

In Bangladesh, traffic is a huge problem, mostly in urban areas like Dhaka. Parking at the roadside is a major problem for traffic in Bangladesh. Private transport user waste most of their time in their daily life searching for a parking slot. Therefore, there is a crucial need to develop a system to help people find vacant parking spots. Systems for managing parking spaces, such as those that identify empty spaces, can help minimize traffic and energy waste in big cities. Since visual approaches may use security cameras installed already in several parking lots, they are an affordable alternative to other methods of vacancy detection. Based on the deep learning model, we have made a cost-effective system and will apply data from that camera. After applying different deep-learning models, we implemented YOLOv7, Mask-RCNN, and YOLOv5. Class loss, Box loss, instances, mAp, and Object loss are all generated by the model. Among all models, YOLOv5 has the highest mAp, 98%. In the future, we will work on providing access to a registered user in the parking garage. This will increase the dataset and obtain higher accuracy.