Pseudo-Labeling Object Detection Labeling Order
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Solution Overview
Problem
Deep learning models for image classification and object detection require a large number of labeled training data, and the variability in labeling quality leads to performance degradation, especially in environments with limited or no labeled images.
Innovation Solution
An apparatus and method for object detection labeling that includes an image selector to determine labeling target images and order, a feedback obtainer to gather user inspection information, and a model trainer that learns this information to generate pseudo labels for supervised learning, using techniques like box proposal networks, spatial pyramid pooling, and gradient reversal layers to reduce information loss and distinguish between unlabeled and labeled images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of labeled training images are used for deep learning, then model performance is improved, but the time and manpower required for labeling increases significantly
Solution Approach 1:
The system performs preliminary clustering and pseudo-labeling on unlabeled images before full labeling is required. By pre-processing images to generate pseudo labels and cluster them, the system prepares data in advance, reducing the actual labeling time when training is needed.
Solution Approach 2:
The system enables unlabeled images to label themselves through self-supervised learning mechanisms. The model trainer generates pseudo labels automatically from the images themselves without human intervention, allowing the data to serve its own labeling function.
2Manufacturing precision
If manual labeling is performed on all images, then labeling quality is improved, but the cost and time consumption increase
Solution Approach 1:
Instead of manually labeling all images, the system applies partial manual verification only to clustered groups of images with similar features. The majority of images receive automated pseudo labels, while only representative samples from each cluster require human inspection, significantly improving efficiency while maintaining quality.
Solution Approach 2:
The system introduces pseudo labels as an intermediary between unlabeled images and final training labels. These pseudo labels serve as a intermediate representation that can be quickly generated and then selectively verified, bridging the gap between automated processing and manual quality control.
3Adaptability or versatility
If diverse labeling styles are used by different operators, then various perspectives are captured, but labeling consistency deteriorates causing model performance degradation
Solution Approach 1:
The system enforces homogeneity in labeling by clustering images with similar features and generating consistent pseudo labels for each cluster. This ensures that all images within a cluster are labeled in the same manner, eliminating variability introduced by different human operators while maintaining adaptability across different image types through the clustering mechanism.
Data Source
AI summary
An apparatus of labeling for object detection according to an embodiment of the present disclosure includes an image selector that determines a plurality of labeling target images from among a plurality of unlabeled images, and determines a labeling order of the plurality of labeling target images, a feedback obtainer that obtains label inspection information on the plurality of labeling target images from a user, and a model trainer that learns the label inspection information input from the user by using the labeling target images, obtains a pseudo label for supervised learning based on a learning result using the label inspection information, and re-determines the labeling order of the labeling target images based on the pseudo label.