Labelling Training System for Object Detection Error Prioritization
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Solution Overview
Problem
Current object detection systems require users to manually review large numbers of images to identify errors, which is time-consuming and inefficient, as only images with errors contribute to model improvement, leading to user complacency and potential oversight of errors in subsequent images.
Innovation Solution
A system that identifies candidate images likely to contain errors, such as false negatives, allowing users to focus on reviewing only those images that have a high likelihood of containing errors, thereby reducing the workload and accelerating model improvement by prioritizing feedback on errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually review all images containing identified objects to identify errors, then the model can be updated with comprehensive error feedback, but the user workload increases significantly and the process becomes time-consuming
Solution Approach 1:
The system performs preliminary analysis by generating processed images with identified objects and calculating false negative likelihood scores before user review. This pre-screening identifies high-priority images that are most likely to contain errors, so users only need to review these pre-selected images rather than all images, significantly reducing review time while maintaining model improvement effectiveness
Solution Approach 2:
The system automatically generates processed images, identifies candidate objects, calculates false negative likelihood scores, and prioritizes images for review without requiring manual intervention. This automation of the triage process allows the system to serve itself in selecting which images need user attention, freeing users from having to manually evaluate all images
2Reliability
If users review images sequentially to identify errors, then all errors can be detected, but user complacency sets in and errors may be overlooked in subsequent images
Solution Approach 1:
Instead of treating all images uniformly, the system applies different quality levels of review based on local characteristics of each image. High-priority images with high false negative likelihood scores receive focused user attention, while low-priority images are either automatically processed or receive minimal review. This differentiated approach maintains high error detection rates without requiring exhaustive review of every image
Solution Approach 2:
The system implements a feedback loop where user corrections are used to update the model, which then generates new processed images with improved object identification. This continuous feedback cycle maintains user engagement by showing tangible improvements in model performance over time, reducing complacency and sustaining attention to error detection
3Reliability
If the model processes all images to identify objects, then comprehensive object detection is achieved, but the concentration of errors in reviewed images decreases, slowing model improvement
Solution Approach 1:
The system segments the image review process into two distinct phases: (1) automated processing that generates processed images and identifies candidate objects for all images to maintain comprehensive detection coverage, and (2) selective user review that focuses only on high-priority images with high false negative likelihood scores. This segmentation allows the system to maintain broad object detection coverage while concentrating user effort on the most error-prone cases, thereby accelerating model improvement
Solution Approach 2:
The system performs partial user review by focusing only on a subset of high-priority images rather than all images. This partial action is sufficient for model improvement because the automated processing already handles comprehensive object identification, and the selective user review provides targeted feedback on the most critical errors, achieving efficient model improvement without exhaustive review
Data Source
AI summary
A method of training a model includes generating processed data by analyzing an input related to an image using a pre-trained model to detect an object in the input. The method includes generating moving area detection data by analyzing the input to determine whether any areas within the input exhibit movement. The method includes generating temporal inconsistency data by analyzing the processed data to determine whether any areas within the processed data exhibit a temporal inconsistency. The method includes identifying whether the input contains an error candidate based on the moving area detection data and the temporal inconsistency data. The method includes receiving confirmation information regarding whether the error candidate is an undetected object within the input. The method includes updating the pre-trained model in response to receiving the confirmation information indicating that the error candidate is the undetected object within the input.