Semantic Knowledge Graph Augmentation for Object Detection
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Solution Overview
Problem
Deep Neural Networks (DNNs) in object detection processes lack the ability to apply context and semantic relationships between objects, leading to unexplainable decision-making and reduced confidence in identification accuracy.
Innovation Solution
An augmented object detection method that utilizes a knowledge graph database to extract semantic links between objects, enhancing confidence scores by analyzing the relationships between identified objects and incorporating these links into the detection process through a context extractor and semantic augmenter module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Deep Neural Networks are used for object detection, then detection performance and scalability are improved, but explainability and context awareness deteriorate
Solution Approach 1:
A knowledge graph is introduced as an intermediary component that bridges the image input and DNN processing. The knowledge graph stores semantic relationships and contextual information about objects, serving as a mediator that provides contextual awareness to the detection system without replacing the core DNN detection architecture.
Solution Approach 2:
The system performs preliminary extraction of semantic relationships and contextual information from the knowledge graph before the main DNN detection process. This preliminary action enriches the detection task with pre-processed semantic context, enabling the DNN to make more informed detection decisions while maintaining its performance advantages.
2Measurement precision
If semantic knowledge graph augmentation is applied, then confidence scores and explainability are improved, but processing complexity increases
Solution Approach 1:
The object detection process is segmented into distinct modules: knowledge graph extraction, semantic relationship identification, confidence score calculation, and final detection output. This segmentation allows each component to handle specific tasks independently, making the overall complex system more manageable and easier to optimize.
Solution Approach 2:
The system uses feedback from the knowledge graph to adjust and refine detection confidence scores. By continuously comparing detected objects against semantic relationships stored in the knowledge graph, the system can validate and adjust its confidence levels, improving measurement precision while providing explainable reasoning for the detection results.
Data Source
AI summary
A method of semantic object detection in an image dataset includes extracting semantic links relevant to the image dataset. Objects are detected in the image dataset and confidence scores are assigned to the detected objects. The semantic object detection compares the detected objects with the semantic links and augments the confidence scores based on the semantic links between the detected objects.