Warehouse Object Movement Detection Using Contour and Texture Analysis
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Solution Overview
Problem
Existing technologies lack an effective method to intelligently determine whether objects have been moved in a warehouse setting, leading to inefficiencies and resource waste in manual inspections.
Innovation Solution
An image processing method that analyzes changes in external contours and internal textures of objects in images taken at different times using trained models for semantic segmentation and edge detection, determining whether any objects have been moved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection is used to determine whether objects have been moved, then human judgment can be applied, but it leads to resource waste and inefficiency
Solution Approach 1:
The patent replaces manual inspection with an automated image processing system that uses semantic segmentation algorithms and edge detection to analyze images and determine whether objects have been moved. This substitution of mechanical/manual processes with automated computational methods directly improves inspection efficiency and eliminates time loss associated with manual checking.
2Productivity
If automated image processing is implemented, then inspection efficiency improves, but the system complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct functional modules: semantic segmentation for identifying object regions, edge detection for contour analysis, and change detection for movement identification. This modular segmentation of the automated system reduces overall complexity by making each component independent and manageable while maintaining high monitoring efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-processing images through semantic segmentation and edge detection before conducting the final movement determination. This preliminary processing organizes the data in advance, simplifying the subsequent analysis step and reducing the complexity of the overall system architecture.
3Measurement precision
If detailed analysis of external contours and internal textures is performed, then movement detection accuracy improves, but processing time increases
Solution Approach 1:
The patent divides the analysis into two parallel segmentation streams: one for external contour analysis using edge detection and another for internal texture analysis using semantic segmentation. By segmenting the analysis tasks and processing them in parallel rather than sequentially, the system achieves high measurement precision through detailed analysis while minimizing processing time through concurrent execution.
Solution Approach 2:
The system performs partial analysis by focusing on key features (external contours and internal textures) rather than analyzing every aspect of the images. This selective partial action maintains high movement detection accuracy while reducing the overall processing time by avoiding unnecessary detailed analysis of all image elements.
Data Source
AI summary
An image processing method includes: acquiring a first image and a second image of a target area, specifically, multiple objects placed in a piled form exist in the target area, and the first image and the second image correspond to different image acquisition moments; according to the first image and the second image, determining first information and second information, specifically, the first information represents a change status in external contours of the multiple objects between the first image and the second image, and the second information represents a change status in internal textures of the multiple objects between the first image and the second image; and according to the first information and the second information, determining whether any of the multiple objects is moved.