GPSD: generative parking spot detection using multi-clue recovery model

Zhihua Chen, Jun Qiu, Bin Sheng, Ping Li, Enhua Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


Due to various complex environmental factors and parking scenes, there are more stringent requirements for automatic parking than the manual one. The existing auto-parking technology is based on space or plane dimension, where the former usually ignores the ground parking spot lines which may cause parking at a wrong position, while the latter often costs a lot of time in object classification which may decreases the algorithm applicability. In this paper, we propose a Generative Parking Spot Detection algorithm which uses a multi-clue recovery model to reconstruct parking spots. In the proposed method, we firstly dismantle the parking spot geometrically for marking the location of its corresponding corners and then use a micro-target recognition network to find corners from the ground image taken by car cameras. After these, we use the multi-clue model to correct the fully pairing map so that the reliable true parking spot can be recovered correctly. The proposed algorithm is compared with several existing algorithms, and the experimental result shows that it has a higher accuracy than others which can reach more than 80% in most test cases.

Original languageEnglish
Pages (from-to)2657-2669
Number of pages13
JournalVisual Computer
Issue number9-11
Publication statusPublished - Sept 2021


  • Auto-parking
  • Corner recognition
  • Multi-clue recovery model
  • Parking spot detection

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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