Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision

Junzhe Jiang, Rongnian Tang, Weian Kang, Zengcai Xu, Cheng Qian

One of the basic requirements of an automated parking system is to quickly and accurately detect parking spaces. With the development of deep convolutional neural networks, parking space detection systems are becoming increasingly mature. However, the existing detection systems are often too complicated, resulting in a long detection time. In order to solve this problem, this paper proposes a two-stage parking space detection system, which aims to combine the advantages of deep learning and traditional computer vision and treat parking space detection as a two-stage process. First, we take the vehicle’s circumnavigation image as input and extract the target box of the parking space key points from the global image through the YoloV11 network. Then, using the special convolution kernel proposed in this paper, the local image is convolved to obtain the direction information of the parking space. Finally, the correct parking space is deduced based on its characteristic parameters. The method in this paper achieved a detection accuracy of 98.24% on the public data set ps2.0 (Parking-slot 2.0), and the parking space inference time on a single image was only 12.3 ms. Compared with the existing methods, the two-stage parking space detection system proposed in this paper not only demonstrates higher robustness, but also significantly reduces the time required for parking space inference.