Runtime Object Detection Ranking for Ground-Truth-Free Mask Selection
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Solution Overview
Problem
Large-scale salient object detection projects face challenges in selecting the best-performing object detector and settings for each image due to the wide variety of image content and characteristics, leading to poorer quality results in downstream image processing tasks.
Innovation Solution
A quality predictor assigns quality scores to segmentation masks generated by different object detectors without using ground truth, identifying a set of highest quality scores for each image, and using these masks for image processing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple object detectors and settings are used to handle diverse image content, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the object detection process by using multiple specialized object detectors, each optimized for specific image characteristics or object types. This allows the complex task of detecting diverse objects across varied images to be divided into manageable sub-tasks handled by individual detectors, thereby improving overall detection accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system implements a universal framework that can accommodate multiple object detectors with different operational settings within a single unified system. This multi-functional approach allows the system to handle diverse image content and characteristics by dynamically selecting and switching between different detectors and settings, improving detection accuracy across various scenarios without requiring separate systems for each task
2Manufacturing precision
If the best object detector and settings are selected for each image, then downstream task quality is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-evaluating and ranking multiple object detectors and their operational settings before actual image processing occurs. By establishing performance benchmarks and quality metrics in advance, the system can quickly select the optimal detector settings for each image without time-consuming real-time evaluation, thereby improving downstream task quality while minimizing processing time delays
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor and evaluate the performance of object detectors on test images with known ground truth. This feedback loop allows the system to learn from past performance, refine its detector selection criteria, and improve future detections. The feedback mechanism enables the system to achieve high downstream task quality by iteratively optimizing detector performance while maintaining efficient processing speeds through learned patterns and heuristics
3Productivity
If quality scores are assigned without ground truth, then system efficiency is improved, but quality assessment accuracy may worsen
Solution Approach 1:
The system employs self-service quality assessment mechanisms where the quality predictor automatically evaluates segmentation masks and assigns quality scores without requiring external ground truth annotations. The system uses its own internal models and learned criteria to assess quality, thereby improving system efficiency by eliminating the need for manual annotation and ground truth comparison. This self-service approach maintains acceptable quality assessment accuracy through continuous training on diverse image datasets and iterative refinement of quality metrics
Data Source
AI summary
Example solutions for ranking object detection results generate or receive a plurality of segmentation masks each corresponding to one or more images. Each segmentation mask of each plurality of segmentation masks is generated using a different object detector or setting options. A quality predictor assigns a quality score to each segmentation mask, without using ground truth for the image(s). A set (one or more, but less than all) of the highest quality scores is identified for each image. In some examples, an image processing task is performed using the segmentation masks having an assigned quality score that is within the set of highest quality scores. In some examples, only the segmentation mask having the highest quality score for an image is used in the image processing task. In some examples, a quality threshold is provided, and the segmentation masks meeting the quality threshold are used in the image processing task.