Infrared Small Target Detection Using Depth Map in Complex Scenes
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Solution Overview
Problem
Existing infrared target detection algorithms face challenges in complex scenes due to low contrast and similarity with background features, environmental noise, and computational complexity, especially when detecting small targets with varying sizes and distances.
Innovation Solution
A method utilizing depth maps from binocular infrared cameras, involving image binarization, morphological processing, and static and dynamic scoring strategies to detect infrared small targets by ranking connected components based on geometric and movement features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional infrared target detection algorithms are used in complex scenes, then detection coverage is maintained, but detection precision deteriorates due to low contrast and background similarity
Solution Approach 1:
The patent introduces depth information as a new dimension beyond traditional 2D infrared image analysis. By fusing depth maps with infrared images, the system creates a 3D-aware detection framework that distinguishes targets from background based on depth differences, thereby maintaining detection coverage while significantly improving precision in complex scenes.
Solution Approach 2:
The patent uses depth maps as an intermediary information source to bridge the gap between infrared signal and target identification. The depth map serves as a mediator that provides geometric context, enabling the system to differentiate true targets from background artifacts that appear similar in infrared alone.
2Productivity
If frame difference algorithm is used, then real-time operation is achieved, but detection precision deteriorates due to inability to detect overlapping targets and sensitivity to environmental noise
Solution Approach 1:
The patent segments the detection process into multiple independent modules: depth map acquisition, infrared image processing, feature extraction, and target detection. This segmentation allows each module to be optimized independently, maintaining real-time performance while improving overall detection precision through specialized processing at each stage.
Solution Approach 2:
The patent merges multiple information sources (infrared intensity, depth distance, motion characteristics) into a unified detection framework. By combining these diverse features, the system achieves both real-time operation and high detection precision, overcoming the limitations of single-feature approaches.
3Adaptability or versatility
If background difference algorithm is used, then adaptability to varying illumination is improved, but detection precision deteriorates due to difficulty in modeling and updating dynamic background
Solution Approach 1:
The patent transitions from 2D background modeling to 3D depth-aware background understanding. By incorporating depth information, the system can distinguish between actual background changes and target presence, maintaining adaptability to illumination changes while improving detection precision even in dynamic environments.
4Measurement precision
If optical flow algorithm is used, then detection precision is improved, but computational complexity increases leading to poor real-time performance
Solution Approach 1:
The patent extracts only the essential motion features needed for detection rather than computing full optical flow fields. By taking out only the critical motion information and combining it with depth constraints, the system achieves high detection precision with reduced computational complexity, enabling real-time operation.
5Reliability
If mean shift algorithm is used, then detection robustness to edge blocking and uneven background motion is improved, but computational complexity increases due to iterative calculation requirements
Solution Approach 1:
The patent performs preliminary filtering and feature selection using depth information before applying complex detection algorithms. By pre-processing the data with depth constraints, the system reduces the search space and computational burden of iterative algorithms like mean shift, maintaining robustness while reducing complexity.
Data Source
AI summary
The present invention discloses a method for infrared small target detection based on a depth map in a complex scene, and belongs to the field of target detection. An infrared image is collected, the image is binarized by using priori knowledge of a to-be-detected target and adopting a pixel value method, the binary image is further limited based on deep priori knowledge, then static and dynamic scoring strategies are formulated to score a candidate connected component in the morphologically processed image, and an infrared small target in a complex scene is detected finally. The method can screen out targets within a specific range, has high reliability; has strong robustness; is simple in program and easy to implement, can be used in sea, land, and air, and has a significant advantage under a complex jungle background.