Coarse-to-Fine Object Detection Using Scene Relations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional object detection methods in computer vision are time-consuming and inefficient, as they require exhaustive search of entire images at different scales, which does not significantly improve detection rates despite reducing processing time by constraining the search space based on prior knowledge.
Innovation Solution
The approach involves object detection based on both the target object's descriptive features and its relationships with other objects in the scene, using a coarse-to-fine strategy to restrict the search space to regions with higher detection rates, such as anchor objects, and increasing the resolution and relative size of search regions to improve detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional exhaustive search methods are used to detect objects at different scales, then detection coverage is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the image processing task into two distinct stages: a coarse detection stage that quickly identifies candidate regions using low-resolution processing, and a fine detection stage that performs detailed object detection only within those constrained regions. This segmentation allows the system to achieve comprehensive detection coverage while significantly reducing overall processing time by avoiding exhaustive search across the entire image.
Solution Approach 2:
The patent performs preliminary coarse detection at low resolution before conducting fine detection at high resolution. By first identifying candidate regions through a quick preliminary scan, the system prepares the groundwork for more detailed detection only where necessary, thereby reducing the total computational burden and processing time while maintaining detection accuracy.
2Loss of time
If search space is constrained based on prior knowledge, then processing time is reduced, but detection rate does not significantly improve
Solution Approach 1:
The patent applies local quality by using different detection strategies for different regions of the image. Candidate regions identified through coarse detection receive detailed fine detection processing, while other regions are processed more simply. This localized approach ensures high detection accuracy in critical areas while maintaining overall processing efficiency.
Solution Approach 2:
The patent introduces a resolution dimension to the detection process by operating at two distinct resolution levels. The coarse detection stage operates at low resolution to quickly identify candidate regions, while the fine detection stage operates at high resolution within those regions. This dimensional approach allows the system to achieve both speed and accuracy without compromising detection rate.
3Reliability
If entire image is explored at different scales, then object detection coverage is improved, but computational complexity increases
Solution Approach 1:
The patent divides the computational task into two segments: coarse detection that processes the entire image at low resolution to identify candidate regions, and fine detection that processes only those candidate regions at high resolution. This segmentation dramatically reduces computational complexity compared to performing exhaustive multi-scale detection across the entire image, while still maintaining comprehensive detection coverage.
Solution Approach 2:
The patent applies partial action by performing exhaustive detection only in candidate regions identified through coarse detection, rather than performing exhaustive detection across the entire image. This partial approach focuses computational resources on the most promising areas, reducing overall computational complexity while maintaining detection coverage for objects that appear in those regions.
Data Source
AI summary
Various embodiments described herein provide for detection of a particular object within a scene depicted by image data by using a coarse-to-fine approach/strategy based on one or more relationships of objects depicted within the scene.