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Second, to facilitate the study of vision-based parking-slot detection, a largescale labeled dataset is established. This dataset is the largest in this field, comprising 12,165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking-points and parking-slots are carefully labeled. Semi-automatic parking system is a driver convenience system automating steering control required during parking operation. This paper proposes novel monocular-vision based target parking-slot recognition by recognizing parking-slot markings when driver designates a seed-point inside the target parking-slot with touch screen.

Vision Detection System

Vision-based parking-slot detection, a largescale labeled dataset is established. This dataset is the largest in this field, comprising 12,165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking-points and parking-slots are carefully labeled.

'Vacant Parking Slot Detection in the Around View Image Based on Deep Learning.' Sensors 20.7 (2020): 2138. 'Vision-Based Parking Slot Detection Based on End-to-End Semantic Segmentation Training.' 2020 IEEE International Conference on Consumer Electronics (ICCE). Second, to facilitate the study of vision-based parking-slot detection, a large-scale labeled dataset is established. This dataset is the largest in this field, comprising 12 165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking points and parking slots are carefully labeled.

Vision-based Parking Slot Detection Based On End-to-end Semantic Segmentation Training

Dataset Download

You can download CNRPark+EXT using the following links:

Vision-based Parking-slot Detection A Benchmark And A Learning-based Approach

Vision Based Parking Slot Detection Software

• CNRPark+EXT.csv (18.1 MB)

  CSV collecting metadata for each patch of both CNRPark and CNR-EXT datasets

• CNRPark-Patches-150x150.zip (36.6 MB)

  segmented images (patches) of parking spaces belonging to the CNRPark preliminary subset.
  Files follow this organization: <CAMERA>/<CLASS>/YYYYMMDD_HHMM_<SLOT_ID>.jpg, where:

  • <CAMERA> can be A or B,
  • <CLASS> can be free or busy,
  • YYYYMMDD_HHMM is the zero-padded 24-hour capture datetime,
  • <SLOT_ID> is a local ID given to the slot for that particular camera

  E.g:A/busy/20150703_1425_32.jpg

• CNR-EXT-Patches-150x150.zip (449.5 MB)

  segmented images (patches) of parking spaces belonging to the CNR-EXT subset.
  Files follow this organization:
  PATCHES/<WEATHER>/<CAPTURE_DATE>/camera<CAM_ID>/<W_ID>_<CAPTURE_DATE>_<CAPTURE_TIME>_C0<CAM_ID>_<SLOT_ID>.jpg,
  where:

  • <WEATHER> can be SUNNY, OVERCAST or RAINY,
  • <CAPTURE_DATE> is the zero-padded YYYY-MM-DD formatted capture date,
  • <CAM_ID> is the number of the camera, ranging 1-9,
  • <W_ID> is a weather identifier, that can be S, O or R,
  • <CAPTURE_TIME> is the zero-padded 24-hour HH.MM formatted capture time,
  • <SLOT_ID> is a global ID given to the monitored slot; this can be used to uniquely identify a slot in the CNR-EXT dataset.

  E.g:PATCHES/SUNNY/2015-11-22/camera6/S_2015-11-22_09.47_C06_205.jpg

  The LABELS folder contains a list file for each split of the dataset used in our experiments. Each line in list files follow this format: <IMAGE_PATH> <LABEL>, where:

  • <IMAGE_PATH> is the path to a slot image,
  • <LABEL> is 0 for free, 1 for busy.

• CNR-EXT_FULL_IMAGE_1000x750.tar (1.1 GB)

  full frames of the cameras belonging to the CNR-EXT subset. Images have been downsampled from 2592x1944 to 1000x750 due to privacy issues.
  Files follow this organization:
  FULL_IMAGE_1000x750/<WEATHER>/<CAPTURE_DATE>/camera<CAM_ID>/<CAPTURE_DATE>_<CAPTURE_TIME>.jpg,
  where:

  • <WEATHER> can be SUNNY, OVERCAST or RAINY,
  • <CAPTURE_DATE> is the zero-padded YYYY-MM-DD formatted capture date,
  • <CAM_ID> is the number of the camera, ranging 1-9,
  • <CAPTURE_TIME> is the zero-padded 24-hour HHMM formatted capture time.

  The archive contains also 9 CSV files (one per camera) containing the bounding boxes of each parking space with which patches have been segmented. Pixel coordinates of the bouding boxes refer to the 2592x1944 version of the image and need to be rescaled to match the 1000x750 version.

• splits.zip (27.2 MB)

  all the splits used in our experiments. Those splits combine our datasets and also third-party datasets (such as PKLot).