Vehicle Detection Model Selection for Factory State Variations
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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 based on appearance, with multiple detection models trained for specific states, and incorporating image processing techniques like distortion correction and rotation to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single detection model is used for all vehicle states, then the device complexity is reduced, but the measurement precision of vehicle detection decreases due to appearance variations across manufacturing steps
Solution Approach 1:
The patent segments the detection task by creating multiple detection models, each specialized for a specific vehicle state (e.g., pre-paint, post-paint, assembly stages). This segmentation allows each model to focus on detecting vehicles with particular appearance characteristics, thereby improving detection accuracy without requiring a single overly complex model to handle all variations
Solution Approach 2:
The patent changes the parameter of detection model selection based on vehicle state. By identifying the current manufacturing state of the vehicle and selecting the corresponding detection model, the system adapts its detection parameters to match the vehicle's appearance characteristics at different stages, improving measurement precision while managing complexity through selective model application
2Measurement precision
If multiple detection models are prepared for different vehicle states, then the measurement precision of vehicle detection is improved, but the device complexity increases due to managing multiple models
Solution Approach 1:
The patent applies preliminary action by pre-training and preparing multiple detection models for different vehicle states before actual detection begins. Each model is trained in advance on images specific to its designated vehicle state, so that during operation, the system can directly select and apply the appropriate pre-prepared model without needing to dynamically adapt or retrain, thereby managing complexity through advance preparation
Solution Approach 2:
The patent introduces an intermediary component that manages the selection and coordination of multiple detection models. This intermediary layer handles the complexity of model management by automatically selecting the appropriate model based on vehicle state identification, isolating the complexity from the main detection workflow and making the system easier to manage and operate
3Measurement precision
If detection models are trained with equal numbers of images for all vehicle states, then the training process is simplified, but the measurement precision decreases for less common vehicle states
Solution Approach 1:
The patent applies local quality by adjusting the training data composition for each detection model based on the specific requirements of its target vehicle state. For less common vehicle states, the training dataset is enriched with more images and diverse examples to ensure adequate learning, while common states may use standard training sets. This localized optimization of training data quality improves detection accuracy for rare states without uniformly increasing complexity across all models
Data Source
AI summary
In a detecting apparatus configured to detect a vehicle in a captured image, the vehicle is configured to move in a factory in which manufacturing steps are performed to manufacture and ship the vehicle, the vehicle is classified into states in accordance with an appearance of the vehicle, varying among the manufacturing steps. The detecting apparatus includes a first processor configured to acquire the captured image, acquire state information indicating one of the states of the vehicle 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 in the captured image by inputting the captured image to the acquired first detection model and identifying a target region representing the vehicle in the captured image.