Multimodal Sample Generation for Intelligent Inspection
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Solution Overview
Problem
Current inspection methods, especially in industrial settings, face challenges such as high costs, low efficiency, and poor accuracy due to the complexity and diversity of production scenarios, with manual inspections being cumbersome and machine vision methods lacking robustness and adaptability.
Innovation Solution
A method for generating a multimodal set of samples using active learning, which involves inputting environmental samples into single-modal models to determine initial samples and then processing them to create a multimodal set, facilitating the training of deep learning models for improved accuracy and adaptability across various scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual inspection methods are used, then flexibility and adaptability to diverse scenarios are maintained, but inspection efficiency is low and labor costs are high
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated multimodal deep learning system that processes images, audio, and text simultaneously. This substitution maintains adaptability through configurable modalities while dramatically improving inspection efficiency and reducing labor costs.
Solution Approach 2:
The inspection system is designed with universal multimodal capabilities, integrating image, audio, and text processing functions into a single platform. This multi-functionality allows the system to adapt to diverse inspection scenarios while operating at high speed and efficiency.
2Productivity
If machine vision methods are used, then inspection efficiency is improved, but robustness and adaptability to complex scenarios deteriorate
Solution Approach 1:
The patent merges multiple sensing modalities (image, audio, text) into a unified inspection system. This combination enhances robustness by providing multiple data sources that complement each other, while maintaining high inspection efficiency through parallel processing of all modalities.
Solution Approach 2:
The system uses a composite approach by integrating multiple types of data (visual, auditory, textual) similar to how composite materials combine different properties. This multimodal fusion creates a more robust inspection system that can handle complex scenarios better than single-modality systems while preserving efficiency.
3Measurement precision
If extensive manual labeling is performed to improve model accuracy, then detection precision is enhanced, but time consumption and costs increase
Solution Approach 1:
The system implements self-service through automated active learning that identifies and selects the most informative samples for labeling. This reduces the overall labeling burden while maintaining high detection precision, as the system intelligently focuses labeling efforts on cases that will most improve model performance.
Solution Approach 2:
The system performs preliminary filtering and selection of samples before the full labeling process. By pre-identifying the most valuable samples for labeling through active learning, it reduces the total time and resources needed for data preparation while ensuring that labeling efforts are concentrated on the most impactful cases.
4Measurement precision
If more training samples are collected to improve model accuracy, then detection precision is enhanced, but data processing complexity and costs increase
Solution Approach 1:
The system extracts and focuses on the most informative subset of training samples using active learning, rather than processing all available data uniformly. This extraction of key samples reduces data processing complexity while maintaining or improving detection precision by concentrating computational resources on the most valuable data.
Data Source
AI summary
A method of generating a multimodal set of samples for an intelligent inspection, and a training method, which relate to a field of an artificial intelligence technology, in particular to fields of deep learning, natural language processing, speech technology, computer vision, big data and so on. The method of generating a multimodal set of samples includes: inputting an environmental sample in a collected multimodal set of environmental samples into a single-modal model matched with a modality of the environmental sample, so as to obtain a model processing result corresponding to the environmental sample; determining an initial set of samples from the multimodal set of environmental samples according to the model processing result; and processing the initial set of samples by means of an active learning, so as to determine the multimodal set of samples.