Vehicle Image Detection by State-Specific Model Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The accuracy of detecting vehicles in manufacturing environments is compromised due to variations in appearance across different manufacturing steps, leading to decreased detection precision.
Innovation Solution
A detecting apparatus utilizing machine learning models to classify vehicle states and select appropriate detection models based on captured image analysis, incorporating distortion correction and orientation processing to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single detection model is used for all vehicle appearances, then device complexity is reduced, but detection precision deteriorates due to appearance variations across manufacturing steps
Solution Approach 1:
The patent divides the detection system into multiple specialized detection models, each trained for specific vehicle appearances at different manufacturing steps. This segmentation allows each model to focus on particular characteristics, improving detection precision while managing complexity through modular architecture
Solution Approach 2:
The system dynamically selects the appropriate detection model based on the current vehicle appearance and manufacturing step. This dynamic adaptation enables the system to optimize detection precision for each specific scenario without being constrained by a single static model
2Measurement precision
If multiple detection models are prepared for different vehicle states, then detection precision is improved, but processing time increases due to model selection and switching
Solution Approach 1:
The system performs preliminary classification of vehicle appearance and manufacturing step before detection. This preliminary action enables rapid model selection, reducing the time penalty associated with having multiple detection models by pre-organizing the selection process
Solution Approach 2:
The patent introduces an intermediate classification mechanism that bridges the gap between diverse vehicle appearances and specific detection models. This intermediary layer efficiently routes input images to the appropriate model, minimizing selection time and maintaining high detection accuracy
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
In a detecting apparatus (5; 5a; 5c; 5d) configured to detect a vehicle (10) in a captured image, the vehicle (10) is configured to move in a factory in which manufacturing steps are performed to manufacture and ship the vehicle (10), the vehicle (10) is classified into states in accordance with an appearance of the vehicle (10), varying among the manufacturing steps. The detecting apparatus (5; 5a; 5c; 5d) includes a first processor (52; 52c; 52d) configured to acquire the captured image, acquire state information indicating one of the states of the vehicle (10) in the captured image, from among first detection models that are machine learning models each prepared one by one for the states, acquire the first detection model selected in accordance with the one of the states, identified by the acquired state information, and detect the vehicle (10) in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle (10) in the captured image.