Nighttime Object Sensing With LWIR-NIR Range and Classification
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Solution Overview
Problem
Nighttime or low-light environments pose challenges for automated vehicle control systems due to limited effective range of visible spectrum sensors, which can reduce safety and maximum safe speed.
Innovation Solution
A combination of long wave infrared and near infrared sensors is employed, with control parameters adjusted to enhance image capture and classification, allowing for earlier detection and classification of objects at longer ranges, thereby improving safety and maximum speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visible spectrum sensors are used for object detection, then the system can operate with simpler sensor configuration, but the effective range is limited in nighttime or low-light environments
Solution Approach 1:
The patent combines long wave infrared sensors and near infrared sensors with visible spectrum sensors to create a multi-modal sensing system. The long wave infrared sensor detects thermal radiation from objects, while the near infrared sensor captures reflected near-infrared light, allowing the system to detect objects at longer ranges in low-light conditions where visible spectrum sensors alone would be insufficient.
2Length of stationary object
If multi-modal sensing with long wave infrared and near infrared sensors is employed, then the effective range for object detection is increased, but the device complexity increases
Solution Approach 1:
The patent segments the sensing system into distinct functional components: long wave infrared sensors for thermal detection, near infrared sensors for reflected light detection, and visible spectrum sensors for color and detail detection. Each sensor type operates in its optimal spectral range, and the system processes each modality separately before fusing the results, making the complex system more manageable and maintainable.
Solution Approach 2:
The patent implements a unified processing apparatus that handles multiple sensor modalities (long wave infrared, near infrared, and visible spectrum) through a single system architecture. This multi-functional processing system can detect, classify, and track objects using any or all sensor types depending on environmental conditions, reducing overall system complexity despite the multiple sensor inputs.
3Measurement precision
If control parameters are adjusted based on region of interest data, then the image classification accuracy is improved, but the processing time increases
Solution Approach 1:
The patent applies local quality by adjusting control parameters of the near infrared sensor specifically for the region of interest rather than uniformly across the entire image. The processing apparatus identifies objects of interest and modifies sensor parameters (such as integration time or gain) only for those specific regions, thereby improving classification accuracy for critical areas while minimizing additional processing time compared to processing the entire image at high resolution.
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 multi-modal sensing approach increases the effective range for object detection and classification in low-light environments, enhancing safety and allowing for higher maximum safe speeds.
Implementation Method 1
obtain a long wave infrared image from the long wave infrared sensor
Implementation Method 2
obtain a near infrared image captured using the adjusted control parameter of the near infrared sensor
Data Source
AI summary
Systems and methods for night vision combining sensor image types. Some implementations may include obtaining a long wave infrared image from a long wave infrared sensor; detecting an object in the long wave infrared image; identifying a region of interest associated with the object; adjusting a control parameter of a near infrared sensor based on data associated with the region of interest; obtaining a near infrared image captured using the adjusted control parameter of the near infrared sensor; and determining a classification of the object based on data of the near infrared image associated with the region of interest.