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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images.

Guan Sheng Wong¹, Kah Ong Michael Goh¹, Connie Tee¹

¹Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This review explores vision-based deep learning methods for accurate parking slot detection in autonomous vehicles. It categorizes techniques like object detection and image segmentation, analyzing their performance on key datasets.

Keywords:

deep learning parking slot detection surround view images

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Accurate parking slot detection is crucial for autonomous vehicle functionality.
Modern parking environments present challenges like varied slot designs, lighting, and obstacles.
Existing detection methods need comprehensive evaluation for real-world autonomous driving.

Purpose of the Study:

To provide a comprehensive review of vision-based deep learning methods for parking slot detection.
To categorize and analyze the strengths and weaknesses of different deep learning approaches.
To assess the performance of these methods on benchmark datasets and identify future research avenues.

Main Methods:

Categorization of deep learning methods into object detection, image segmentation, regression, and graph neural networks.
Detailed explanation of the unique features and capabilities of each method category.
Performance analysis using the Tongji Parking-slot Dataset 2.0 (ps 2.0), Sejong National University (SNU) dataset, and panoramic surround view (PSV) dataset.

Main Results:

Different deep learning categories exhibit varying performance characteristics for parking slot detection.
Object detection and image segmentation show promise but face challenges with complex scenarios.
Regression and graph neural networks offer alternative approaches with specific advantages.
Dataset performance analysis highlights the impact of dataset diversity and size.

Conclusions:

Vision-based deep learning offers powerful tools for autonomous parking slot detection.
Further research is needed to address challenges related to diverse parking conditions and real-time performance.
Advancements in deep learning architectures and training strategies are essential for robust autonomous parking systems.