Object Recognition Using Reflective Light Blocking
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Solution Overview
Problem
Current object recognition systems face challenges in accurately identifying fluorescent objects under real-world conditions, particularly due to the difficulty in separating reflected and fluorescent light, which requires costly hyperspectral cameras and known lighting conditions, and are resource-intensive and prone to errors in ambient lighting.
Innovation Solution
A system utilizing a light source with narrowband LEDs, a sensor unit with color-sensitive sensors and camera filters to physically separate reflected and fluorescent light, and a processing unit to identify objects based on acquired data and pre-defined luminescence properties, allowing for operation under varying geometries and ambient lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral cameras and controlled lighting conditions are used to separate reflected and fluorescent light, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the light detection task by using separate sensors for different wavelength ranges. A first sensor detects reflected light in the first wavelength range, while a second sensor detects fluorescent light in a second wavelength range. This segmentation allows accurate light separation using simpler, less expensive sensors rather than requiring a complex hyperspectral camera system.
Solution Approach 2:
The patent introduces an intermediary substance (fluorescent marker or pigment) that absorbs light in the first wavelength range and emits light in the second wavelength range. This intermediary enables the separation of reflected and fluorescent light signals by creating a distinct spectral signature that can be detected by separate sensors, avoiding the need for complex hyperspectral analysis.
2Measurement precision
If computational methods are used to separate reflected and fluorescent light, then measurement precision is improved, but use of energy and processing resources increase
Solution Approach 1:
The patent performs preliminary action by physically separating the detection of reflected and fluorescent light at the sensor level rather than mixing the signals and attempting to separate them computationally later. The first sensor is configured to detect only reflected light while the second sensor detects only fluorescent light, eliminating the need for energy-intensive computational separation algorithms.
Solution Approach 2:
The patent replaces the computational method (software-based light separation) with a physical/optical method (separate sensors with wavelength-selective detection). This substitution reduces processing energy requirements by performing the separation function in the optical domain rather than requiring intensive computational analysis of mixed signals.
3Adaptability or versatility
If luminescence-based recognition is performed under ambient lighting, then adaptability is improved, but measurement precision deteriorates due to mixed reflected and luminescent components
Solution Approach 1:
The patent segments the detection task by using a first sensor to detect ambient reflected light and a second sensor to detect fluorescent emission. By separating these detections, the system can operate under ambient lighting conditions while maintaining precision, as each sensor measures only its designated component without interference from the other.
Solution Approach 2:
The fluorescent marker acts as an intermediary that converts ambient light into a distinct fluorescent signal detectable by the second sensor. This intermediary enables the system to operate under ambient lighting by creating a separable signal signature that distinguishes fluorescent objects from their background, maintaining measurement precision despite the ambient lighting environment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high accuracy and low latency in object recognition with reduced resource usage, enabling effective identification of fluorescent objects in complex environments without relying on expensive equipment or predefined lighting conditions.
Implementation Method 1
a camera filter selectively blocking the reflected light and allowing passage of luminescence originating from illuminating the scene with the light source into the at least one color sensitive sensor
Implementation Method 2
camera filter selectively blocking the reflected light originating from illuminating the scene with the light source
Data Source
AI summary
Disclosed herein are methods and systems for object recognition utilizing reflective light blocking. Further disclosed herein are systems and methods for recognition of at least one fluorescent object being present in a scene by using a light source including at least one illuminant and a bandpass filter for each illuminant of the light source, a sensor array including at least one light sensitive sensor and at least one filter selectively blocking the reflected light originating from illuminating the scene with the light source and allowing passage of luminescence originating from illuminating the scene with the light source into the at least one color sensitive sensor, and a processing unit for identifying the at least one object based on the data detected by the sensory array and known data on luminescence properties associated with known objects.