Monocular Camera Focus Ranging for Segmented Object Detection
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Solution Overview
Problem
Current range detection methods for autonomous agents are costly and less accurate when using monocular cameras, as they require significant computational resources and are not as precise as LIDAR sensors or stereo camera systems.
Innovation Solution
A method that segments images into segmentation blobs using a monocular camera and focuses on specific blobs to estimate the range by utilizing the camera's focus function, allowing for accurate distance calculation based on the focus value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR sensors or stereo camera systems are used for range detection, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the image into multiple regions of interest (ROIs) based on object detection results. By focusing computational resources only on these segmented regions rather than processing the entire image, the system achieves accurate range detection comparable to LIDAR while using a simpler monocular camera system. The segmentation divides the complex task of full-image depth analysis into manageable regional analyses.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image. High-precision focus analysis is applied only to pixels within detected object ROIs, while other regions receive minimal or no processing. This local quality approach maintains measurement precision for critical objects while reducing overall device complexity and computational requirements.
2Device complexity
If monocular cameras are used for range detection, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary object detection and image segmentation before conducting focus analysis. By pre-identifying regions of interest and preparing ROI masks in advance, the monocular camera system can concentrate its analytical power on specific objects, thereby achieving accurate range detection without requiring complex sensor hardware.
Solution Approach 2:
The patent replaces the mechanical/optical complexity of LIDAR sensors or stereo camera systems with a computational approach using a simple monocular camera. Instead of using multiple physical sensors or complex optical mechanisms, the system uses image processing algorithms including object detection, segmentation, and focus analysis to achieve range detection functionality.
3Measurement precision
If deep neural networks are used for object identification, then object detection accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The patent extracts only the essential computational components needed for the specific task at hand. Rather than running full deep neural networks for complete image analysis, the system extracts and processes only the detected object regions using focus analysis. This extraction approach maintains object detection accuracy while significantly reducing computational energy consumption by eliminating unnecessary processing of background and irrelevant regions.
4Measurement precision
If full image processing is performed for range detection, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent segments the image into discrete regions of interest based on object detection. This segmentation allows the system to perform focus analysis only on relevant object regions rather than processing every pixel in the entire image. The result is maintained measurement precision for detected objects while dramatically improving processing speed and productivity.
Solution Approach 2:
The patent applies partial action by performing complete focus analysis only on detected object regions rather than on the entire image. This selective partial processing maintains accuracy for critical objects while reducing overall computational load and improving processing throughput, thereby enhancing productivity without sacrificing measurement precision where it matters most.
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
This approach enables efficient and accurate range detection using low-cost monocular cameras, providing reliable distance information for object detection and tracking in autonomous systems, improving planning and control functionalities.
Implementation Method 1
determining a distance to the selected segmentation blob according to a focus function value of the monocular camera of the ego vehicle
Data Source
AI summary
A method for range detection is described. The method includes segmenting an image into one or more segmentation blobs captured by a monocular camera of an ego vehicle. The method includes focusing on pixels forming a selected segmentation blob of the one or more segment blobs. The method also includes determining a distance to the selected segmentation blob according to a focus function value of the monocular camera of the ego vehicle.