Deep Learning Model for Bounding Box Labeling Inspection
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Solution Overview
Problem
Manual inspection of labeling operations on bounding boxes in images is inefficient and time-consuming, affecting the accuracy and efficiency of deep learning model training.
Innovation Solution
A method using a deep learning model to automatically or semi-automatically inspect labeling operations on bounding boxes, involving first training the model, calculating an inspection score based on the model's output, and determining the accuracy of the labeling operation to perform pass, fail, or re-inspection processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of labeling operations is performed, then accuracy of labeling can be maintained, but time consumption and inefficiency increase
Solution Approach 1:
The patent replaces the manual mechanical inspection process with an automated deep learning model that processes images and bounding box labeling values. The model calculates inspection scores automatically, eliminating the need for human operators to manually verify each labeling, thus reducing inspection time while maintaining accuracy through automated object recognition and comparison algorithms.
Solution Approach 2:
The system creates a digital copy of the labeling verification process through the deep learning model, which replicates the inspection function without requiring physical human intervention. The model generates inspection scores based on learned patterns from training data, effectively copying the expert verification capability in software form, enabling parallel processing of multiple labelings simultaneously.
2Productivity
If automated inspection using deep learning model is implemented, then efficiency increases, but system complexity increases
Solution Approach 1:
The system performs preliminary training of the deep learning model using labeled training images before actual inspection. This pre-processing step prepares the model with learned patterns and thresholds, enabling it to perform automated inspection efficiently during operation. The preliminary action of training reduces the complexity of real-time decision-making during actual labeling verification.
Solution Approach 2:
The deep learning model acts as an intermediary between the labeling operation and the inspection result. It receives operation images and bounding box labeling values as input, processes them through learned features, and outputs inspection scores. This intermediary layer simplifies the overall system by encapsulating the complex inspection logic within the trained model, providing a clean interface for automated verification.
3Measurement precision
If deep learning model is trained with more data, then model accuracy improves, but training time and computational resources increase
Solution Approach 1:
The system uses a sufficient but not excessive amount of training data to achieve the required inspection accuracy. The deep learning model is trained on a curated set of training images with ground truth labeling values that provide adequate coverage of the inspection scenarios. This partial action approach achieves the necessary model accuracy without investing in unnecessarily large training datasets, optimizing the trade-off between model performance and training resource consumption.
Data Source
AI summary
According to the present invention, proposed is a method for inspecting a labeling operation, the method comprising, when a deep learning model for inspecting a labeling operation for a bounding box corresponding to an object included in an image is present and a computing apparatus uses the deep learning model, the steps of: performing, by the computing apparatus, first training on the deep learning model on the basis of a training image; obtaining, by the computing apparatus, an operation image and a bounding box labeling value therefor; calculating, by the computing apparatus, a score for inspection by performing a calculation while passing the operation image and the bounding box labeling value through the deep learning model; and determining, by the computing apparatus, whether the bounding box labeling value for the operation image is accurate on the basis of the score for inspection and performing any one of a pass process, a fail process, and a re-inspection process.