Parking Space and Obstacle Detection Based on a Vision Sensor and Checkerboard Grid Laser
Open accessAbstract
The accuracy of automated parking technology that uses ultrasonic radar or camera vision for obstacles and parking space identification can easily be affected by the surrounding environment especially when the color of the obstacles is similar to the ground. Additionally, this type of system cannot recognize the size of the obstacles detected. This paper proposes a method to identify parking spaces and obstacles based on visual sensor and laser device recognition methods by installing a laser transmitter on the car. The laser transmitter produces a checkerboard-shaped laser grid (mesh), which varies with the condition encountered on the ground, which is then captured by the camera and taken as the region of interest for the necessary image processing. The experimental results show that this method can effectively identify obstacles as well as their size and parking spaces even when the obstacles and the background have a similar color compared to when only using sensors or cameras alone.
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Ma et al.'s multi-sensor-based parking space recognition method can enhance parking space resources by identifying parking spots in more complex situations.
“This technique can enhance parking space resources by identifying parking spots in more complex situations.”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [103]
The Introduction says the vision-and-laser method can identify parking spaces and obstacles and improve recognition of suitable spaces; Section 4.4 describes identifying valid spaces without parking lines. This supports detection in more challenging situations, but the paper does not establish that the method enhances parking-space resources or their utilization. (LLM, conf=0.82)
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Holistically-Nested Edge Detection
Xie, S., & Tu, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.164 · Cited by 3,401 · see citing works
Flexible camera calibration by viewing a plane from unknown orientations
Zhang, Z. (1999) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1999.791289 · Cited by 2,690 · see citing works
Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution
Huang, S.-C., Cheng, F., & Chiu, Y.-S. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2226047 · Cited by 1,236 · see citing works
Automatic camera and range sensor calibration using a single shot
Geiger, A., Moosmann, F., Car, O., & Schuster, B. (2012) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224570 · Cited by 669 · see citing works
Real time finger tracking and contour detection for gesture recognition using OpenCV
Gurav, R. M., & Kadbe, P. K. (2015) · doi:10.1109/iic.2015.7150886 · Cited by 91 · see citing works
Ibisch, A., Stümper, S., Altinger, H., et al. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629569 · Cited by 82 · see citing works
Obstacle detection using a 2D LIDAR system for an Autonomous Vehicle
Catapang, A. N., & Ramos, M. C. (2016) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce.2016.7893614 · Cited by 81 · see citing works
Research on Automatic Parking Systems Based on Parking Scene Recognition
Ma, S., Jiang, H., Han, M., et al. (2017) · IEEE Access · doi:10.1109/access.2017.2760201 · Cited by 72 · see citing works
Self-citationAutomatic Parking Space Detection System
Bibi, N., Majid, M. N., Dawood, H., et al. (2017) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1109/icmip.2017.4 · Cited by 70 · see citing works
Analysis and review of state-of-the-art automatic parking assist system
Song, Y., & Liao, C. (2016) · IEEE International Conference on Vehicular Electronics and Safety (ICVES) · doi:10.1109/icves.2016.7548171 · Cited by 46 · see citing works
Dual-fisheye lens stitching for 360-degree imaging
Ho, T., & Budagavi, M. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952541 · Cited by 44 · see citing works
Camera self-calibration with lens distortion
Sun, Q., Wang, X., Xu, J., et al. (2016) · Optik · doi:10.1016/j.ijleo.2016.01.123 · Cited by 41 · see citing works
AUTOMATIC PARKING OF SELF-DRIVING CAR BASED ON LIDAR
Lee, B., Wei, Y., & Guo, I. Y. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w7-241-2017 · Cited by 36 · see citing works
A convex hull approach in conjunction with Gaussian mixture model for salient object detection
Singh, N., Arya, R., & Agrawal, R. K. (2016) · Digital Signal Processing · doi:10.1016/j.dsp.2016.05.003 · Cited by 35 · see citing works
A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles
Suhr, J. K., & Jung, H. G. (2018) · Sensors · doi:10.3390/s18041213 · Cited by 34 · see citing works
Image Enhancement Using Fuzzy Technique
Asthana, T. M. A. A. (2013) · Cited by 28 · see citing works
Removal of fixed value impulse noise using improved mean filter for image enhancement
Gupta, V., Gandhi, D. K., & Yadav, P. (2013) · Nirma University International Conference on Engineering · doi:10.1109/nuicone.2013.6780117 · Cited by 28 · see citing works
Light Stripe Projection based Parking Space Detection for Intelligent Parking Assist System
Jung, H. G., Kim, D. S., Yoon, P. J., et al. (2007) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2007.4290241 · Cited by 25 · see citing works
Parking space detection from a radar based target list
Prophet, R., Hoffmann, M., Vossiek, M., et al. (2017) · IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM) · doi:10.1109/icmim.2017.7918864 · Cited by 25 · see citing works
Novel Survey on the Color-Image Graying Algorithm
Zhang, X., & Wang, X. (2016) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2016.32 · Cited by 15 · see citing works
A Survey of Obstacle Detection Using Vision Sensor for Autonomous Vehicles
Park, J., Lee, J., & Son, S. H. (2016) · IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA) · doi:10.1109/rtcsa.2016.54 · Cited by 14 · see citing works
A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars
Luo, Q., Saigal, R., Hampshire, R. C., & Wu, X. (2017) · IEEE Vehicular Technology Conference · doi:10.1109/vtcspring.2017.8108418 · Cited by 11 · see citing works
Sensor fusion‐based precise obstacle localisation for automatic parking systems
Suhr, J. K., & Jung, H. G. (2018) · Electronics Letters · doi:10.1049/el.2018.0196 · Cited by 8 · see citing works
Intelligent Identification of Automatic Parking System Based on Information Fusion
Jiang, H. (2017) · Journal of Mechanical Engineering · doi:10.3901/jme.2017.22.125 · Cited by 4 · see citing works
Self-citation
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Research works printed but not linked: 3
- Chen, F. Research on Automatic Parking Technology Based on Machine Vision. Master's Thesis, Electronic Science and Technology Univ., Chengdu, China, 2016.
- Shi, X.L. Research of Automatic Parking System Based on Laser Radar. Master's Thesis, Shanghai Jiao Tong Univ., Shanghai, China, 2010.
- Zhang, J.; Wang, D.; Ma, L. The self-calibration technology of camera intrinsic parameters calibration methods. J. Imaging Sci. Photochem. 2016, 34, 15-22.
Cited by
Sorted by citations
Raouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M. M., & Kim, H. S. (2022) · Mathematics · doi:10.3390/math10183233 · Cited by 41 · see citing works
- Authors
- Ma, S., Jiang, Z., Jiang, H., Han, M., & Li, C.
- Affiliations
- Jiangsu University (CN)
- Corresponding author
- Han, Mu
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2020-04-09
- Volume
- 10
- Issue
- 7
- Pages
- 2582
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3015317608
- Cited by
- 10
- References
- 24
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 1
- FWCI
- 0.74
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Science Fund for Colleges and Universities in Jiangsu Province (12KJD580002, 16KJA580001)
- Innovation Plan for Postgraduate Research of Jiangsu Province in 2014 (KYLX1057)
- National Natural Science Foundation of China (51675235)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Building and Construction › Smart Parking Systems Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- autonomous parking, parking space detection, obstacle detection, laser transmitter, vision sensors, region of interest, image processing
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