Parking Space Detection With Multi-Model Verification
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Solution Overview
Problem
Existing parking space detection methods using image classification are unreliable in complex scenarios due to obstacles and non-standard elements, leading to inaccurate availability determination.
Innovation Solution
A method involving a pre-trained parking space detection model, obstacle detection model, and scenario detection model, combined with positional relationship analysis, to verify parking space availability through multiple verification mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If image classification method is used for parking space detection, then the detection process is simple, but the reliability of parking space availability determination deteriorates in complex scenarios
Solution Approach 1:
The detection process is segmented into multiple independent detection models: a parking space detection model for identifying parking spaces, an obstacle detection model for detecting obstacles, and a scenario detection model for recognizing scenario types. Each model specializes in one aspect, improving overall reliability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
Multiple detection results are merged through a verification mechanism that combines parking space prediction results, obstacle prediction results, and scenario prediction results. The system merges these independent detection outcomes to comprehensively determine parking space availability, resolving the contradiction between simple process and reliable determination.
2Measurement precision
If multiple verification mechanisms are implemented, then the detection precision is improved, but the system complexity increases
Solution Approach 1:
The verification mechanism is segmented into distinct verification paths: one path verifies parking space results using both obstacle and scenario prediction results, while another path verifies using only scenario prediction results. This segmentation allows precise verification without requiring all components to interact complexly, maintaining manageable system complexity.
Solution Approach 2:
The system performs preliminary detection actions by obtaining parking space prediction results, obstacle prediction results, and scenario prediction results separately before the final verification step. This preliminary action organization reduces the complexity of the verification process by preparing all necessary information in advance.
3Measurement precision
If the vehicle must fully pass the target parking space for accurate detection, then the detection accuracy is improved, but the parking space release rate deteriorates
Solution Approach 1:
The system performs partial verification action by using scenario prediction results to verify parking space availability without requiring the vehicle to fully pass the target parking space. This partial action is sufficient to determine availability in many cases, improving the parking space release rate while maintaining acceptable detection accuracy through the multi-model verification approach.
Data Source
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AI summary
The disclosure provides a parking space detection method and device, a vehicle, and a storage medium. The method includes: separately inputting an obtained current frame image into a pre-trained parking space detection model, a pre-trained obstacle detection model, and a pre-trained scenario detection model, to obtain a parking space prediction result, an obstacle prediction result, and a scenario prediction result; determining, based on a detected positional relationship between any target parking space and a vehicle-mounted camera, whether the target parking space is a parking space where the vehicle-mounted camera is located; performing, if yes, verification on a parking space prediction result of the target parking space by using an obstacle prediction result and a scenario prediction result, to obtain a single-frame prediction result of the target parking space; and performing, if no, verification on a parking space prediction result of the target parking space by using a scenario prediction result, to obtain a single-frame prediction result of the target parking space. In this way, after the verification based on the plurality of verification mechanisms, a highly precise parking space detection result is given in a complex scenario, and a precise prediction result is given while the vehicle does not need to pass the target parking space completely, which improves a parking space release rate.