Camera-based intelligent parking system using object detection algorithms (region-based convolutional neural networks).

As the number of vehicles increases day by day, finding an empty parking space to park a vehicle becomes difficult. Car parking can cause wasted time and interfere with surrounding mobility, as a result the parking area cannot be utilized optimally. The existing parking management system using sensors to detect the available parking spaces is less effective and efficient, for example a system with the use of ultrasonic sensors that must be placed in each parking box will require many sensors in large-scale implementation. The proposed intelligent parking system provides a structured solution by using a parking lot camera available in the campus or office area to observe the used parking area and using image processing to detect the available parking space from the camera in real time. From the results of image processing available parking spaces will be recommended to users through a front-end system based on the closest distance that helps drivers park their vehicles. The proposed system improves the overall effectiveness and efficiency of the current parking system and solves the problem that drivers spend a lot of time in finding suitable parking spaces in crowded campus or office parking areas. The architecture of the intelligent parking system includes three stages: the first stage of the system uses sensors to capture images of the parking area and sends them to the database server in real time; the second stage of the proposed method uses object detection algorithms (ie, Region-based Convolutional Neural Networks) to identify whether parking spaces in the building area are available or not and calculate their utility; the third stage a front-end system was developed for drivers to get real-time parking information by using a monitor placed at the entrance gate of a campus or office parking area. [ABSTRACT FROM AUTHOR]