Multi-Mode Edge Fusion for Object Detection
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Solution Overview
Problem
Existing machine vision systems face difficulties in detecting edges of objects, particularly when objects are stacked or packed, due to limitations in color or optical resolution, leading to challenges in identifying gaps and dimensions.
Innovation Solution
The system employs multiple modes of image capture, such as 2D and 3D imaging, to generate and fuse edge information, improving edge detection accuracy by combining data from different imaging devices to enhance object detection and tracking in industrial settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single imaging device is used for object detection, then device complexity is reduced, but detection reliability deteriorates due to inability to detect contrast or boundaries between objects
Solution Approach 1:
The imaging system is segmented into multiple independent imaging devices, each capturing data in a specific mode (e.g., 2D, 3D, depth). Each device processes information independently and the results are fused later, allowing the system to achieve high detection reliability without requiring a single complex device to perform all functions
Solution Approach 2:
Multiple imaging devices are employed where each device serves a specific detection function (color detection, depth detection, edge detection). This multi-functionality approach allows the system to overcome the limitations of any single device while maintaining overall system reliability without requiring one overly complex device
2Measurement precision
If multiple modes of image capture are used to improve edge detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The imaging system is divided into separate modules, each dedicated to capturing specific types of data (first mode image data, second mode image data). This segmentation allows each module to be optimized for its specific function while the overall system achieves high measurement precision through the combination of specialized data sources
Solution Approach 2:
Edge information from multiple imaging modes is merged through a fusion process that combines the strengths of each mode. The system merges first edge information with second edge information to generate fused edge information, achieving high measurement precision by integrating complementary data from different imaging perspectives
3Reliability
If multiple imaging devices are deployed to detect object boundaries, then detection reliability improves, but processing time increases due to data fusion requirements
Solution Approach 1:
The system extracts and processes only the essential edge information from each imaging mode rather than processing all raw image data. By taking out and focusing on edge information specifically, the system reduces processing time while maintaining the reliability benefits of multi-device detection
Solution Approach 2:
Edge detection and information extraction are performed preliminarily on each imaging mode before the fusion process. This preliminary action prepares the data in advance, reducing the computational burden during fusion and thereby decreasing overall processing time while maintaining detection reliability
Data Source
AI summary
A system and method of detecting objects are provided. The method includes generating first edge information from first image data representing an object based on a first mode of image capture, generating second edge information from second image data representing the object based on a second mode of image capture, the second mode being different from the first mode, fusing the first edge information with the second edge information to generate fused edge information, generating an object detection hypothesis based on the fused edge information, and validating the object detection hypothesis based on the fused edge information, the first edge information, and/or the second edge information.