Edge Diversity Object Detection in Environmental Interference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting objects in images fail to distinguish between objects of interest and their surroundings, particularly in environments with environmental effects like fog, moisture, smog, smoke, and solar glint, leading to false detection due to strong edge intensities from unwanted features.
Innovation Solution
A method that assigns each pixel to orientation bins based on its orientation value, determines the maximum pixel magnitude values for each bin in a surrounding region, sums these values, and generates an updated image with thresholds to identify object regions, while aggregating the image using functions of ground sample distance and solar glint angle to reduce unwanted features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional edge detection methods are used, then edges from objects of interest can be detected, but edges from unwanted features (surroundings, environmental effects) are also detected and often appear stronger, leading to false positives
Solution Approach 1:
The patent segments edge detection by orientation, dividing the edge detection space into multiple orientation bins (e.g., 0-45°, 45-90°, etc.). Instead of treating all edges equally, it processes different orientation ranges separately, allowing the system to distinguish between edges of objects of interest and edges from unwanted features based on their directional characteristics
Solution Approach 2:
The patent applies local quality by analyzing the distribution of edge orientations at each pixel location. It calculates the ratio of edges in different orientation bins within a local neighborhood, creating a signature that is unique to each location. This allows the system to identify whether a pixel belongs to an object of interest or to unwanted features based on the local orientation distribution pattern
2Measurement precision
If image intensity levels are used for detection, then objects can be identified, but environmental effects (solar glint, fog, moisture, smog, smoke, exhaust plumes) create similar intensity patterns that confuse the detection system
Solution Approach 1:
The patent adds the orientation dimension to traditional intensity-based detection. Instead of relying solely on intensity magnitude, it incorporates the orientation of edges as a separate dimension of analysis. This dimensional expansion allows the system to distinguish between intensity patterns caused by objects of interest and those caused by environmental effects, as they typically have different orientation characteristics
Solution Approach 2:
The patent changes the detection parameters from simple intensity magnitude to a combination of orientation angle and intensity. By transforming the detection space to include orientation information, the system can identify objects based on their characteristic edge orientations rather than being fooled by intensity similarities to environmental features
Data Source
Figure 1
Figure 2A~2B
Figure 2C~2D
AI summary
Methods for detecting objects in an image. The method includes a) receiving magnitude and orientation values for each pixel in an image and b) assigning each pixel to one of a predetermined number of orientation bins based on the orientation value of each pixel. The method also includes c) determining, for a first pixel, a maximum of all the pixel magnitude values for each orientation bin in a predetermined region surrounding the first pixel. The method also includes d) summing the maximum pixel magnitude values for each of the orientation bins in the predetermined region surrounding the first pixel, e) assigning the sum to the first pixel and f) repeating steps c), d) and e) for all the pixels in the image.