Depth Image Depression Detection via Mean-by-Row Graph
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Solution Overview
Problem
Existing machine vision technologies have low accuracy and efficiency in detecting depressions or objects below the horizontal line, limiting their application in scenarios like road surface detection and navigation.
Innovation Solution
The method involves acquiring a depth image, processing it to obtain a mean-by-row graph, determining a road area, identifying suspected depression regions, and judging their presence based on a depression threshold, using a cloud-based processing device and computer program product.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine vision technologies rely on color, shape, and edge information for detection, then the detection process is simple, but the accuracy in detecting depressions or objects below the horizontal line is low
Solution Approach 1:
The patent transitions from 2D image processing to 3D depth information processing by introducing depth maps and spatial coordinate transformations. This dimensional change enables accurate detection of depression regions by utilizing the Z-axis depth data, which provides vertical dimension information that 2D color and shape analysis cannot capture.
Solution Approach 2:
The patent changes the detection parameter from 2D visual features (color, shape, edge) to 3D spatial parameters (depth, height, vertical position). By transforming the detection space into a three-dimensional coordinate system and using depth thresholds, the system achieves high accuracy in detecting depression regions while maintaining computational efficiency.
2Measurement precision
If depth image processing is used to detect depression regions, then detection accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent segments the depth image processing into distinct stages: coordinate transformation, depression region detection, and threshold judgment. By dividing the processing pipeline into modular segments, the system can efficiently handle depth data without excessive computational overhead, reducing processing time while maintaining high detection accuracy.
Solution Approach 2:
The patent performs preliminary coordinate transformation and depth map generation before the actual depression detection. By pre-processing the depth image and establishing the three-dimensional coordinate system in advance, the system reduces the computational complexity during the critical detection phase, thereby minimizing processing time while preserving detection accuracy.
Data Source
AI summary
An information processing method, device, cloud-based processing device, and computer program product are related to the field of data processing technologies and can cause an increased efficiency in detecting whether a road area contains a depression region. The information processing method includes: acquiring a depth image; processing the depth image to obtain a means-by-row graph, based on which a road area in the depth image is determined; determining a suspected depression region in the road area; and judging over the suspected depression region based on a depression threshold to determine whether the depth image contains a depression region.