Object Detector Analysis via Custom Bounding Boxes
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Solution Overview
Problem
Existing object detection systems are complex 'black-boxes' making it difficult to pinpoint errors, especially when objects are misidentified or undetected, due to issues like poor region proposals and non-max suppression, lacking transparency in their internal mechanisms.
Innovation Solution
A method that allows a user to draw custom bounding boxes around objects of interest, run inference operations, and analyze the output to understand the behavior of the object detector, including exempting proposals from non-max suppression and varying bounding box margins to provide contextual data, enabling insights into the detection process without retraining the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If non-max suppression is applied to reduce the number of proposals, then the detection speed and efficiency are improved, but the ability to detect all potential objects is reduced
Solution Approach 1:
The patent applies partial suppression by selectively suppressing only those proposals that have high overlap with already selected detections, while preserving proposals that might represent distinct objects. This partial action approach maintains detection speed by suppressing redundant proposals while improving reliability by keeping potentially valid detections that would otherwise be eliminated by aggressive suppression.
2Measurement precision
If the object detector uses complex machine learning models to improve detection accuracy, then the detection precision is improved, but the interpretability of the system is reduced
Solution Approach 1:
The patent implements feedback mechanisms that provide information about the detection process back to the user. By showing which proposals were selected, which were suppressed and why, and allowing users to review and adjust results, the system maintains high detection accuracy while restoring interpretability through informative feedback loops that reveal the internal decision-making process.
Solution Approach 2:
The patent introduces an intermediary layer between the complex machine learning model and the user. This intermediary provides tools for visualizing proposals, adjusting suppression thresholds, and reviewing detection results, thereby mediating between the black-box nature of complex models and the user's need for interpretability without compromising detection accuracy.
3Reliability
If the region proposal network proposes more regions to ensure all objects are detected, then the detection completeness is improved, but the processing time and computational cost increase
Solution Approach 1:
The patent generates a sufficient number of proposals to ensure all objects are detected, then applies partial suppression to eliminate redundant proposals. This approach ensures detection completeness by initially proposing enough regions while maintaining efficiency through selective suppression of only the excessive, redundant proposals rather than generating fewer proposals from the start.
Data Source
AI summary
A method is described for analyzing an output of an object detector for a selected object of interest in an image. The object of interest in a first image is selected. A user of the object detector draws a bounding box around the object of interest. A first inference operation is run on the first image using the object detector, and in response, the object detect provides a plurality of proposals. A non-max suppression (NMS) algorithm is run on the plurality of proposals, including the proposal having the object of interest. A classifier and bounding box regressor are run on each proposal of the plurality of proposals and results are outputted. The outputted results are then analyzed. The method can provide insight into why an object detector returns the results that it does.