Parking Space Recognition Using Historical Data in Poor Environments
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Solution Overview
Problem
Parking space recognition methods are adversely affected by environmental conditions, leading to reduced efficiency and accuracy, particularly in deteriorated or poorly lit parking lots.
Innovation Solution
A method that utilizes both real-time and historical parking lot information to determine parking space confidence, where the confidence is based on a preset threshold, incorporating historical data to assist in recognizing parking spaces even in challenging environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only real-time parking lot information is used for parking space recognition, then the system complexity is low, but the recognition accuracy deteriorates in poor environmental conditions
Solution Approach 1:
The system performs preliminary action by collecting and storing historical parking lot information in advance under various environmental conditions. This historical data is then utilized during real-time recognition to compensate for poor current environmental conditions, thereby improving recognition accuracy without requiring complex real-time processing infrastructure.
Solution Approach 2:
Historical parking lot information serves as an intermediary element between the current degraded real-time data and the required recognition accuracy. By comparing and fusing historical data with real-time data, the system bridges the gap caused by poor environmental conditions, enabling accurate parking space identification even when real-time imaging conditions are suboptimal.
2Speed
If real-time parking space recognition is performed under deteriorated environmental conditions, then the response speed is fast, but the recognition accuracy deteriorates
Solution Approach 1:
The system merges real-time parking lot information with historical parking lot information to create a fused dataset for recognition. This combination allows the system to maintain fast real-time response while leveraging historical data to compensate for environmental degradation, thereby achieving both speed and accuracy simultaneously.
Solution Approach 2:
The system implements feedback by continuously comparing real-time recognition results with historical data patterns. When environmental conditions deteriorate, the feedback mechanism allows the system to adjust its recognition parameters based on historical success patterns, maintaining accuracy without sacrificing real-time response capability.
3Ease of operation
If parking space lines are blurred or incomplete due to environmental factors, then the ease of operation is maintained, but the measurement precision deteriorates
Solution Approach 1:
The system creates a virtual copy of the parking lot layout by fusing real-time imaging data with historical parking space information. This virtual model allows the system to identify parking spaces even when physical lines are blurred or incomplete, maintaining operational simplicity while achieving accurate detection through data replication and comparison.
Data Source
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AI summary
Provided are a parking space recognition method, apparatus, and device and a storage medium. The method includes acquiring (S110) real-time parking lot information of a target parking lot and historical parking lot information of the target parking lot; determining (S120) target parking space confidence based on real-time parking space data in the real-time parking lot information and historical parking space data in the historical parking lot information; and determining (S130) a parking space corresponding to the target parking space confidence satisfying a preset condition as a target parking space, where the preset condition includes that the target parking space confidence is greater than or equal to a preset threshold.