Image Object Detection via Semantic and Spatial Relation Graphs
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Solution Overview
Problem
Existing image object detection methods are cumbersome and low in accuracy, making it difficult to effectively identify and locate objects in images, which negatively impacts user engagement and network traffic.
Innovation Solution
A method involving region segmentation, feature extraction, generation of semantic and spatial distribution relation graphs, and determination of a target image region based on these graphs to accurately identify and display the object, enhancing user experience and network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image object detection methods are used, then the detection process is simple, but the detection accuracy is low and the process is cumbersome
Solution Approach 1:
The patent divides the image into multiple regions of interest through region segmentation, then performs feature extraction and graph generation on each region separately. This segmentation approach improves detection accuracy by focusing on specific areas while managing complexity through modular processing of individual regions rather than the entire image at once.
Solution Approach 2:
The patent transforms image region data into graph structures (semantic relation graphs and spatial distribution graphs) by adding relational dimensions. This dimensionality change from pixel-based 2D images to graph-based relational representations enables more accurate object detection by capturing semantic and spatial relationships, resolving the accuracy-complexity contradiction.
2Reliability
If accurate object detection is achieved, then user engagement increases, but the detection method becomes more complex and computationally intensive
Solution Approach 1:
By segmenting the image into regions of interest and processing each region independently through feature extraction and graph generation, the patent achieves reliable object detection that improves user engagement. The segmentation strategy manages computational complexity by dividing the problem into smaller, more tractable sub-problems that can be processed efficiently.
Solution Approach 2:
The patent introduces graph structures (semantic relation graphs and spatial distribution graphs) as intermediary representations between raw image data and detection results. These intermediary graphs capture complex relationships in a structured format, improving detection reliability and user engagement while managing computational complexity through efficient graph-based processing.
3Measurement precision
If region segmentation and graph generation are performed, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs region segmentation to divide the image into multiple regions of interest, then processes each region through feature extraction and graph generation. This segmentation improves detection accuracy by focusing computational resources on specific areas, while managing processing time by avoiding unnecessary processing of the entire image uniformly.
Solution Approach 2:
The patent applies feature extraction and graph generation selectively to segmented regions of interest rather than processing the entire image uniformly. This partial action approach achieves high detection accuracy in critical areas while reducing overall processing time and computational resource requirements compared to exhaustive full-image processing.
Data Source
AI summary
The embodiments of this disclosure disclose an image object detection method, device, electronic equipment, and computer-readable medium. A specific mode of carrying out the method includes: performing region segmentation on a target image to obtain at least one image region; performing feature extraction on each image region in the at least one image region to obtain at least one feature map; generating a semantic relation graph and a spatial distribution relation graph based on the at least one feature map and the at least one image region; generating an image region relation graph based on the semantic relation graph and spatial distribution relation graph; determining a target image region from the at least one image region based on the image region relation graph; displaying the target image region. This implementation mode achieves an improvement of user experience and a growth of network traffic.