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Title	Vision-Based Parking Space Detection: A Mask R-Cnn Approach
ID_Doc	61235
Authors	Song Y.; Zeng J.; Wu T.; Ni W.; Liu R.P.
Year	2021
Published	2021 IEEE/CIC International Conference on Communications in China, ICCC 2021
DOI	http://dx.doi.org/10.1109/ICCC52777.2021.9580236
Abstract	The rapid increment of vehicles and the inefficient management of available parking spaces lead to traffic congestion and resource waste in urban areas. Thus, there is an urgent need to develop an intelligent parking system to find out suitable parking spaces quickly. To this end, we elaborate on various object detection algorithms and parking space detection methods. Then, we propose a novel vision-based parking space detection system with a Mask R-CNN approach. It can be applied in various scenarios and infer parking spaces from the positions of the parked vehicles. Experimental results have shown that the proposed system performs well in large car parks and reduces the human effort in image processing. This study provides a successful paradigm for future intelligent parking systems, and it can also effectively promote the development of smart cities. © 2021 IEEE.
Author Keywords	Instance segmentation; Object detection; Parking space detection; Region-based convolutional neural networks(R-CNN)

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