Thermal Sensor Parameter Tuning for ROI Object Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and predicting the behavior of objects in various environmental conditions, particularly at night, in heavy rain or snow, where thermal sensors provide clearer data but can be affected by factors like temperature ranges and urbanization, leading to reduced clarity and increased difficulty in object detection.
Innovation Solution
A parameter adjustment component that adjusts thermal sensor parameters, such as dynamic range, contrast, and gain settings, based on map data and environmental conditions, to improve object detection by focusing on regions of interest and excluding areas like the sky, thereby enhancing the clarity of thermal sensor data for safer navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If thermal sensor parameters are adjusted to improve object detection in challenging environments, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system pre-adjusts thermal sensor parameters based on environmental conditions (night, rain, snow, urbanization) before object detection begins. By anticipating challenging conditions and pre-configuring optimal parameters for dynamic range, contrast, and gain, the system improves detection accuracy without adding real-time processing complexity during critical detection phases.
Solution Approach 2:
The system dynamically changes thermal sensor parameters including dynamic range, contrast, and gain settings based on detected environmental conditions. This allows the sensor to adapt to varying temperatures, precipitation, and urban heat island effects, improving object detection accuracy across diverse conditions while maintaining a relatively simple adjustment mechanism.
2Loss of information
If the thermal sensor monitors the entire field of view, then comprehensive data is captured, but processing resources are consumed
Solution Approach 1:
The system applies different processing quality levels to different regions of the thermal sensor field of view. By identifying regions of interest (such as areas with detected objects or areas requiring enhanced monitoring) and applying enhanced parameter adjustments locally, the system maintains data completeness where needed while reducing processing resources in less critical areas.
Solution Approach 2:
The field of view is segmented into multiple regions, with the system applying selective parameter adjustments to specific segments rather than uniformly processing the entire view. This allows comprehensive monitoring of critical regions while conserving processing resources in other areas, balancing data completeness with resource efficiency.
Data Source
AI summary
Techniques associated with generating or tuning parameters associated with long wave infrared sensor data to improve object detection associated with the captured images are discussed herein. The system may determine a region of interest associated with the sensor data and adjust or tune the parameters to improve detection(s) within the region of interest. Additionally, the system may adjust the parameters based on map data and/or environmental conditions, such as weather and temperature.