A New Approach to Detecting Free Parking Spaces Based on YOLOv6 and Keyframe Detection with Video Analytics

With urbanization and population growth, automobile use has also increased. As a result of this, there is a need to expand parking management and inspection systems to ensure safe and efficient use of parking areas. A new deep learning-based method has been proposed as an alternative to traditional systems to control free spaces in parking areas monitored by CCTV security cameras. A large amount of video data needs to be processed for parking areas recorded with video cameras 24 hours a day, 365 days a year. Repeating frames in videos cause overlearning in deep learning models. It also increases memory and computation cost. For this reason, keyframe detection based on sampling was first performed on the sample video of the Dragon Lake Parking (DLP) dataset and a summary of the dataset was obtained. Then, empty parking areas were detected by YOLOv6 deep neural network. It has been observed that the model can detect empty parking areas at a rate of 96.6% mAP.