Object Detection Using Dynamic Background Image Integration
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Solution Overview
Problem
Existing object detection techniques require a background image for accurate detection, but this is impractical when it's unavailable, and previous methods fail to effectively utilize a background image even when it is present.
Innovation Solution
An object detection system that acquires both a main image and a background image, using separate models to calculate feature maps from each, allowing for accurate object detection by combining these maps when the background image is available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a background image is always required for object detection, then detection accuracy can be improved, but the system cannot operate when a background image is unavailable
Solution Approach 1:
The system dynamically adapts its operation mode based on the availability of background images. When a background image is available, the system uses difference information from both the main image and background image to improve detection accuracy. When a background image is unavailable, the system seamlessly transitions to using only the main image for detection, ensuring continuous operation. This dynamic adaptability resolves the contradiction between requiring background images for accuracy and needing to operate without them.
2Measurement precision
If a background image is used in combination with a main image, then detection accuracy is improved, but the system becomes more complex
Solution Approach 1:
The system segments the image processing into distinct functional components: a main image processing branch and a background image processing branch. Each branch has its own feature extraction and detection mechanisms. This segmentation allows the system to independently process different image types and combine their results, improving detection accuracy while maintaining modular architecture that manages complexity.
Solution Approach 2:
The detection system is designed with multi-functionality to handle both single-image and dual-image input modes. The same detection framework can operate with only a main image or with both main image and background image, making the system universal. This multi-functionality allows accurate detection using background information when available, while avoiding unnecessary complexity when background images are not used.
3Measurement precision
If separate models are used for main image and background image processing, then detection accuracy is improved, but computational resources increase
Solution Approach 1:
The system implements partial action by conditionally processing only the necessary image inputs. When a background image is unavailable, the system processes only the main image through the first model, avoiding the computational overhead of the second model. When a background image is available, the system activates both models to leverage difference information for improved accuracy. This partial processing approach balances detection accuracy with computational resource consumption.
Data Source
AI summary
In order to achieve object detection with high accuracy by additionally using an image such as a background image in accordance with a situation, an object detection apparatus (1) includes: an image acquisition section (11) that acquires a first image; a calculation section (12) that uses a first model to calculate a first map from the first image; and a detection section (13) that carries out object detection with reference to at least the first map, in a case where the image acquisition section (11) acquires not only the first image but also a second image, the calculation section (12) using a second model to calculate a second map from the second image or from the first image and the second image, and the detection section (13) carrying out object detection with reference to not only the first map but also the second map.