Vehicle Head Position Detection via Deep Learning Reconstruction
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Solution Overview
Problem
Current vehicle identification methods are inefficient and pose safety risks due to the need to capture entire vehicle images to determine the vehicle head position, leading to slow identification speeds and potential misjudgments, especially when dealing with specific vehicle types.
Innovation Solution
A method using a deep learning algorithm to reconstruct vehicle body images and identify the boundary between the vehicle head and other parts, allowing for accurate and rapid identification of the vehicle head position without requiring full image capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire vehicle body image is captured to identify the vehicle head position, then the identification accuracy is improved, but the identification speed decreases and the time consumption increases
Solution Approach 1:
The patent extracts only the necessary information (vehicle head position) from the vehicle image sequence without capturing or processing the entire vehicle body image. By using image sequence analysis and trajectory prediction, the system directly identifies the vehicle head position and movement trend, eliminating the need for full image capture and complex image processing, thus resolving the contradiction between identification accuracy and speed.
Solution Approach 2:
The patent performs preliminary actions by capturing a sequence of images before the vehicle reaches the inspection point and uses these pre-captured images to predict the vehicle head position and movement trajectory. This allows the system to identify the vehicle head position in advance without waiting for the vehicle to be fully imaged, thereby improving identification speed while maintaining accuracy.
2Measurement precision
If the entire vehicle body image is captured to identify the vehicle head position, then the identification accuracy is improved, but the time consumption increases
Solution Approach 1:
The patent extracts only the necessary information (vehicle head position) from the vehicle image sequence without capturing or processing the entire vehicle body image. By using image sequence analysis and trajectory prediction, the system directly identifies the vehicle head position and movement trend, eliminating the need for full image capture and complex image processing, thus resolving the contradiction between identification accuracy and time consumption.
Solution Approach 2:
The patent performs preliminary actions by capturing a sequence of images before the vehicle reaches the inspection point and uses these pre-captured images to predict the vehicle head position and movement trajectory. This allows the system to identify the vehicle head position in advance without waiting for the vehicle to be fully imaged, thereby reducing time consumption while maintaining accuracy.
3Device complexity
If rule-based methods are used to identify vehicle head position from images, then the system complexity is reduced, but the identification accuracy decreases and misjudgment risk increases
Solution Approach 1:
The patent introduces an intermediary element (image sequence analysis combined with trajectory prediction algorithm) between the image capture and the vehicle head position identification. Instead of directly using rule-based image processing, the system uses the sequence of images and movement trajectory as an intermediary to infer the vehicle head position, which improves accuracy while keeping the system relatively simple.
Solution Approach 2:
The patent replaces the mechanical rule-based image processing system with a trajectory-based prediction system. Instead of analyzing image pixels and vehicle features directly, the system substitutes this with a method that tracks vehicle movement over time and predicts position based on trajectory, thereby improving accuracy without significantly increasing system complexity.
4Device complexity
If rule-based methods are used to identify vehicle head position, then the system complexity is reduced, but the misjudgment risk increases
Solution Approach 1:
The patent introduces an intermediary element (image sequence analysis combined with trajectory prediction algorithm) between the image capture and the vehicle head position identification. Instead of directly using rule-based image processing, the system uses the sequence of images and movement trajectory as an intermediary to infer the vehicle head position, which improves reliability while keeping the system relatively simple.
Solution Approach 2:
The patent replaces the mechanical rule-based image processing system with a trajectory-based prediction system. Instead of analyzing image pixels and vehicle features directly, the system substitutes this with a method that tracks vehicle movement over time and predicts position based on trajectory, thereby improving reliability without significantly increasing system complexity.
Data Source
AI summary
The present disclosure discloses a method and system for identifying a part of a vehicle and a vehicle inspection system. The method includes: acquiring a vehicle body image sequence of a vehicle to be identified; reconstructing the vehicle body by using a first vehicle body reconstruction model generated through a deep learning algorithm and on the basis of the vehicle body image sequence, so as to acquire a vehicle body reconstruction image of the vehicle to be identified; and identifying a boundary identifier of the vehicle to be identified on the basis of the vehicle body reconstruction image of the vehicle to be identified.