Detection of Parking Slots Based on Mask R-CNN

Jiang, Shaokang; Jiang, Haobin; Ma, Shidian; Jiang, Zhongxu

doi:10.3390/app10124295

Cited by 17 publications

(15 citation statements)

References 39 publications

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“…At present, most visual algorithms are based on the vision features---corner features and line features, through some lowlevel visual algorithms (such as fast detector, Harris detector, Hough transform, Radon transform, RANSAC transform), which are sensitive to light and difficult to maintain robustness. Therefore, in order to solve the above problems, Jiang et al (2020) proposed a parking space marking detection method based on deep learning. This method uses the mask-RCNN algorithm (He et al, 2017) to generate the mask of marking points, and then uses the line segment detection (LSD) algorithm to detect the mask and filter the interference lines, and to find the guide line and the separating line to determine the final candidate parking space.…”

Section: Image-segmentation-based Methodsmentioning

confidence: 99%

Review of Research on Vision-Based Parking Space Detection Method

Liu

Shao

et al. 2022

International Journal of Web Services Research

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Parking space detection is an important part of the automatic parking assistance system. How to use existing sensors to accurately and effectively detect parking spaces is the key problem that has not been solved in the automatic parking system. Advances in Artificial Intelligence and sensing technologies have motivated significant research and development in parking space detection in the automotive field. Firstly, based on extensive investigation of a lot of literature and the latest re-search results, this paper divides parking space detection methods into methods based on traditional visual features and those methods based on deep learning and introduces them separately. Secondly, the advantages and disadvantages of each parking space detection method are analyzed, compared, and summarized. And the benchmark datasets and algorithm evaluation standards commonly used in parking space detection methods are introduced. Finally, the vision-based parking space detection method is summarized, and the future development trend is prospected.

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Section: Image-segmentation-based Methodsmentioning

confidence: 99%

Review of Research on Vision-Based Parking Space Detection Method

Liu

Shao

et al. 2022

International Journal of Web Services Research

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“…In addition, a parking-slot-marking detection approach based on deep learning is proposed. The detection process involves the generation of mask of the marking-points by using the Mask R-CNN algorithm, extracting parking guidelines and parallel lines on the mask using the line segment detection (LSD) to determine the candidate parking slots [29]. However, it is not easy to ensure the recognition accuracy when the parking angle and parking line are worn or blocked.…”

Section: Parking Space Recognition Algorithmmentioning

confidence: 99%

Parking Space Recognition Method Based on Parking Space Feature Construction in the Scene of Autonomous Valet Parking

Fang

Jiang

et al. 2021

Applied Sciences

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At present, the realization of autonomous valet parking (AVP) technology does not achieve information interaction between the parking spaces and vehicles, and accurate parking spaces information perception cannot be obtained when the accuracy of the search is not precise. In addition, when using the camera vision to identify the parking spaces, traditional parking space features such as parking lines and parking angles recognition are susceptible to light and environment. Especially when the vehicle nearby partially occupies the parking space to be parked, it is not easy to determine whether it is a valid empty parking space. This paper proposes a parking space recognition method based on parking space features in the scene of AVP. By constructing the multi-dimensional features containing the parking space information, the cameras are used to extract features’ contour, locate features’ position and recognize features. In this paper, a new similarity calculation formula is proposed to recognize the stained features through template matching algorithm. According to the relative position relationship between the feature and parking space, the identification of effective empty parking spaces and their boundaries is realized. The experimental results show that compared with the recognition of traditional parking lines and parking angles, this method can identify effective empty parking spaces even when the light conditions are complex and the parking spaces are partially occupied by adjacent vehicles, which simplifies the recognition algorithm and improves the reliability of the parking spaces identification.

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“…By equating the equation ( 19 ) to zero and being nonlinear can be solved numerically for obtaining to, therefore, we obtained the second derivative of equation ( 18 ) with respect to to:…”

Section: Periodical Preventive Maintenance Schedulementioning

confidence: 99%

“…Step 7: Using normal approximation method to construct the two sided confidence limits with confidence levels and of the acceleration factor and the two parameters are constructed Step8: Using Bootstrap method to construct the two sided confidence limits for parameters at Step 9: using the estimated parameters and confidence limits to predict Periodical Preventive Maintenance Schedule as in equations (18)(19)(20)(21)(22)(23) at .…”

Section: Numerical Studymentioning

confidence: 99%

Periodical Preventive Maintenance Schedule for Minimum Cost Using Geometric Process Model in Accelerated life Testing With Exponentiated Weibull Failure Data

2018

مجلة الدراسات المالیة والتجاریة

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Maintenance may be considered the heath care of our manufacturing machines and equipment. It is required to effectively reduce waste and run an efficient, continuous manufacturing operation, business, or service operation. The planned maintenance of equipment will help to improve equipment life. The goal of this paper is to estimate the Periodical Preventive Maintenance Schedule which minimize the total cost of maintenance. The research concern on estimating the parameters of time failures Exponentiated Weibull distribution using Geometric process model in accelerated life testing. Maximum likelihood method is used to estimate the model parameters and acceleration factor of lifetime distribution from the test data. Confidence Interval for the model parameters are constructed using normal approximation and bootstrap method. we used the estimated parameters to obtain periodical preventive maintenance schedule which minimizes the total maintenance cost. Finally, Some numerical illustrations are provided .

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Detection of Parking Slots Based on Mask R-CNN

Cited by 17 publications

References 39 publications

Review of Research on Vision-Based Parking Space Detection Method

Review of Research on Vision-Based Parking Space Detection Method

Parking Space Recognition Method Based on Parking Space Feature Construction in the Scene of Autonomous Valet Parking

Periodical Preventive Maintenance Schedule for Minimum Cost Using Geometric Process Model in Accelerated life Testing With Exponentiated Weibull Failure Data

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