Knowledge-Enhanced Object Detection for Overlapping Objects
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Solution Overview
Problem
Conventional object detection methods struggle to accurately identify objects in images with many overlapping objects, leading to suboptimal detection results.
Innovation Solution
An object detection method that utilizes convolution processing and knowledge graph information to enhance image features, incorporating semantic information from associated objects, improving detection accuracy by combining initial and enhanced image features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional separate object detection methods are used, then the detection process is simple, but the detection accuracy deteriorates when there are many overlapping objects
Solution Approach 1:
The patent segments the detection process into multiple stages: initial detection to identify candidate objects, relationship graph construction to model associations between objects, and refined detection using relationship features. This segmentation allows the system to handle complex overlapping scenarios by breaking down the problem into manageable steps, improving detection accuracy without overwhelming computational complexity.
Solution Approach 2:
The patent introduces a relationship graph as an intermediary structure that captures semantic associations between detected objects. This graph serves as a mediator that enriches the detection process by providing contextual information about object relationships, enabling more accurate detection of overlapping objects through semantic reasoning rather than direct pixel-level analysis.
2Measurement precision
If only initial image features are used for detection, then the processing speed is fast, but the detection precision deteriorates
Solution Approach 1:
The patent performs preliminary detection to identify candidate objects and construct a relationship graph before final classification. This preliminary action extracts semantic relationships and contextual information that enhance the final detection accuracy. By preparing relationship features in advance, the system avoids reprocessing raw image data, thus improving precision without excessive time loss.
Solution Approach 2:
The patent transitions from two-dimensional image pixel analysis to a graph-based representation that captures semantic relationships between objects. This dimensional change from spatial features to relational features provides additional contextual information that improves classification precision, while the graph structure enables efficient computation that mitigates the time cost.
3Measurement precision
If detailed feature analysis is performed on all objects, then the detection accuracy improves, but the computational energy consumption increases
Solution Approach 1:
The patent applies local quality by performing detailed feature analysis only on candidate objects identified in the preliminary detection stage, rather than all objects in the image. The relationship graph enables selective refinement of features for objects that are likely to be overlapping or ambiguous, concentrating computational energy where it is most needed to improve precision without unnecessary energy expenditure on clear-cut cases.
Data Source
AI summary
This application provides example object detection methods and apparatuses. This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. One example method includes obtaining a to-be-detected image and performing convolution processing on the to-be-detected image to obtain an initial image feature of a to-be-detected object in the to-be-detected image. Based on knowledge graph information, an enhanced image feature of the to-be-detected object is determined. A candidate frame and a classification of the to-be-detected object is determined based on the initial image feature and the enhanced image feature of the to-be-detected object. The enhanced image feature indicates semantic information of a different object category corresponding to another object associated with the to-be-detected object.