AI Wear-Style Similarity Detection for Clothing Standardization
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Solution Overview
Problem
The high costs and low accuracy of clothing standardization detection due to the need for customized training of classifiers in different scenarios, as well as the difficulty in identifying wear styles accurately without proper training, are significant challenges.
Innovation Solution
A clothing standardization detection method using a general feature identification capability AI model that processes video and reference sub-images to determine similarities between wear styles, reducing the need for scenario-specific training and improving accuracy by utilizing skeleton joint points and confidence-based image extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier is trained in a customized manner for wearable clothings of a style, then the accuracy of clothing standardization detection is improved, but the training costs increase
Solution Approach 1:
The patent applies universality by designing a classifier that can detect multiple clothing styles through a single unified model architecture. The system uses a shared backbone network that processes images for different clothing types (helmets, masks, gloves, etc.) without requiring separate customized classifiers for each style, thereby reducing training costs while maintaining detection accuracy across various clothing categories
Solution Approach 2:
The patent employs segmentation by dividing the clothing detection task into distinct target parts (head, upper body, lower body, hands). Each target part has specific clothing requirements that are detected independently through the unified classifier, allowing the system to handle diverse clothing styles through modular processing rather than requiring complete retraining for each scenario
2Quantity of substance
If the classifier is not trained for wearable clothings of a style, then the training costs are reduced, but the accuracy of clothing standardization detection deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the unified classifier on a diverse dataset covering multiple clothing styles and scenarios before deployment. The model is preliminarily configured with general feature extraction capabilities that enable it to accurately detect various clothing types without requiring scenario-specific retraining, thus maintaining high accuracy while avoiding additional training costs
Solution Approach 2:
The patent utilizes parameter changes by adjusting the confidence thresholds and detection parameters of the unified classifier based on different clothing types and scenarios. The system dynamically modifies detection parameters such as confidence levels and matching criteria to maintain high accuracy across diverse clothing styles without requiring model retraining, thereby preserving detection precision while keeping training costs low
Data Source
AI summary
This disclosure relates to a clothing standardization detection method. In an example method, a clothing standardization detection apparatus obtains a video frame sub-image and a reference sub-image. The video frame sub-image includes an image of a first wear style of a target part of the target object in the first scenario, and the reference includes an image of a standard wear style of a target part of the reference object in the first scenario. Then, the video frame sub-image and the reference sub-image are processed by using a target model, to obtain a first processing result. The target model is a trained artificial intelligence AI model, and the first processing result indicates a similarity between the first wear style of the target part of the target object and the standard wear style of the target part of the reference object.