Optical Object Detection via Contrast Analysis
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Solution Overview
Problem
Existing object detection methods for autonomous robotic devices are often unreliable outdoors due to limitations in infrared and ultrasonic sensors, and lidar systems are costly and resource-intensive.
Innovation Solution
A computer-readable storage medium with instructions for detecting objects using high-pass filtered images and contrast parameter analysis, which determines object presence based on image processing and actuates robotic actions for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If infrared optical proximity sensing methods are used, then object detection capability is provided, but reliability deteriorates when used outdoors or in presence of other infrared radiation sources
Solution Approach 1:
The patent replaces infrared optical sensing with a mechanical/optical hybrid approach using a camera lens and image sensor to capture visual images, then processes these images through high-pass filtering and contrast analysis to detect objects. This substitution avoids interference from infrared radiation sources while maintaining object detection capability.
2Reliability
If ultrasonic proximity sensors are used, then object detection capability is provided, but reliability deteriorates outdoors due to acoustic noise from motors and environmental factors
Solution Approach 1:
The patent replaces ultrasonic acoustic sensing with optical imaging using a camera lens and image sensor. This substitution eliminates susceptibility to acoustic noise from motors and environmental factors, providing reliable object detection in outdoor conditions.
3Reliability
If lidars are used, then object detection capability is provided, but device complexity and cost increase
Solution Approach 1:
The patent uses inexpensive, commercially available camera lenses and image sensors instead of costly lidar systems. The solution processes images through software algorithms (high-pass filtering and contrast analysis) to achieve reliable object detection at a fraction of the cost and complexity of lidar systems.
4Reliability
If traditional image processing methods are used, then object detection is provided, but processing resources and time increase
Solution Approach 1:
The patent extracts only the essential features from images by applying high-pass filtering to isolate edges and contrast variations, then analyzes only these extracted features for object detection. This approach avoids processing entire images, significantly reducing computational energy consumption while maintaining detection accuracy.
Solution Approach 2:
The patent applies partial processing by focusing computational efforts only on regions of interest within images where contrast parameters indicate potential objects. This selective processing reduces overall energy consumption compared to analyzing entire images.
Data Source
AI summary
An optical object detection apparatus and associated methods. The apparatus may comprise a lens (e.g., fixed-focal length wide aperture lens) and an image sensor. The fixed focal length of the lens may correspond to a depth of field area in front of the lens. When an object enters the depth of field area (e.g., sue to a relative motion between the object and the lens) the object representation on the image sensor plane may be in-focus. Objects outside the depth of field area may be out of focus. In-focus representations of objects may be characterized by a greater contrast parameter compared to out of focus representations. One or more images provided by the detection apparatus may be analyzed in order to determine useful information (e.g., an image contrast parameter) of a given image. Based on the image contrast meeting one or more criteria, a detection indication may be produced.