Dynamic Threshold Target Detection in Vehicle Imagery
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Solution Overview
Problem
Current target detection methods in images from optical sensors, especially for small targets like ballistic missiles, face challenges due to varying signal-to-noise ratios and registration errors, leading to inconsistent detection performance and high false alarm probabilities.
Innovation Solution
A method that estimates the background and statistical characteristics of the estimation error for each pixel, allowing for dynamic threshold determination based on local conditions to improve detection performance and reduce false alarms, using a Kalman filter and noise modeling to account for photonic and electronic noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-end optical sensor is used to reduce the dimensions of image portions, then the resolution is improved, but the complexity of the observation system and its cost increase
Solution Approach 1:
The patent uses image processing to create a processed version of the original image that enhances target visibility. Instead of relying solely on expensive high-resolution sensors, the system processes the captured images through algorithms that simulate the effect of higher resolution by enhancing edges and contrasting features, thereby achieving improved measurement precision without increasing device complexity
Solution Approach 2:
The patent transforms the image data by applying various processing parameters such as contrast enhancement, edge detection, and noise filtering. These parameter changes allow the system to extract target information more effectively from lower-resolution images, achieving the resolution improvement goal through software-based parameter optimization rather than hardware upgrades
2Ease of operation
If a fixed threshold is used for target detection, then the detection process is simple, but the probability of false alarm varies with signal-to-noise ratio
Solution Approach 1:
The patent implements dynamic threshold adjustment where the detection threshold is no longer fixed but adapts to local image conditions. The system calculates thresholds based on the statistical properties of the background and noise in different regions of the image, allowing the threshold to vary spatially and temporally. This dynamic approach maintains detection simplicity while significantly reducing false alarms by adapting to changing signal-to-noise ratios
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and image characteristics are fed back into the threshold determination process. By continuously monitoring the distribution of pixel values and detection outcomes, the system adjusts thresholds in real-time to maintain optimal detection performance, creating a closed-loop system that balances simplicity with reliability
3Measurement precision
If background subtraction is performed to detect targets, then target visibility is improved, but detection performance varies with observed scene
Solution Approach 1:
The patent applies local quality enhancement by performing background subtraction and thresholding operations that are adapted to local image characteristics. Instead of using global parameters, the system analyzes and processes each region according to its specific background properties, noise levels, and signal characteristics. This local adaptation ensures consistent detection performance across diverse observed scenes including different terrains, atmospheric conditions, and target types
Solution Approach 2:
The system performs preliminary background modeling and characterization before actual target detection. By pre-processing the images to establish baseline background statistics and characteristics, the system prepares the data in advance to facilitate more accurate and consistent target detection across varying scenes. This preliminary action reduces the impact of scene variability on detection performance
Data Source
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AI summary
The present invention relates to a method for detecting a target in at least one image consisting of a plurality of pixels, referred to as the "detection image," wherein the detection is performed by evaluating (30), in each pixel to be evaluated, a detection test against at least one threshold. The value of the at least one threshold depends on the pixel to be evaluated, and the method comprises, for each pixel to be evaluated, the steps of: - (10) estimating a background of the detection image at that pixel and estimating statistical characteristics of the error in estimating the background of the image at that pixel, - (20) determining the value of the at least one threshold at that pixel as a function of the estimated background of the image at that pixel and as a function of the estimated statistical characteristics of the error in estimating the background of the image at that pixel.