3D Object Recognition Using Superpixel Segmentation and Depth Data
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Solution Overview
Problem
Existing object recognition methods in images, particularly in unmanned supermarkets, face challenges with low recognition accuracy and high network parameter counts due to the inability to effectively differentiate between objects with similar shapes but different volumes, as 2D image features cannot directly convey volume information.
Innovation Solution
The method involves acquiring images with a binocular camera to calculate depth information, performing superpixel segmentation, and generating 3D image data, which is then input into a depth neural network for object recognition, utilizing down-sampling and up-sampling layers with interpolation and transposed convolution techniques to improve recognition accuracy and reduce network parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If two-dimensional image features are used for object recognition, then the recognition process is simple, but the recognition accuracy is low and cannot differentiate objects with similar shapes but different volumes
Solution Approach 1:
The patent transforms 2D image data into 3D point cloud data by calculating depth information from binocular camera images. This dimensional transformation enables the system to capture volume information that is invisible in 2D images, allowing accurate differentiation of objects with similar shapes but different volumes (e.g., 2L vs 500mL beverage packages). The 3D spatial coordinates (x, y, z) provide the additional dimension needed for precise object identification.
Solution Approach 2:
The patent applies superpixel segmentation to divide the 3D point cloud data into meaningful regions or objects. This segmentation process groups related 3D points together based on spatial proximity and feature similarity, creating discrete object representations that can be individually classified. The segmentation enables the network to process complex scenes by breaking them down into manageable components.
2Productivity
If traditional 2D image recognition methods are used, then the network model has fewer parameters, but the recognition speed is slow and accuracy is low
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with a neural network-based system that directly processes 3D point cloud data. The depth neural network is trained to recognize objects in 3D space, eliminating the need for complex 2D image processing pipelines and enabling faster, more accurate recognition. The network learns optimal feature representations from 3D data, achieving both speed and accuracy improvements.
Solution Approach 2:
The patent changes the fundamental parameters of the input data from 2D coordinates (x, y) to 3D coordinates (x, y, z), adding the depth dimension as a new parameter. This parameter change enables the system to capture volume information that is completely lost in 2D imaging. The additional z-coordinate parameter provides direct measurement of object volume and spatial positioning, transforming the recognition problem into a more informative 3D space.
3Measurement precision
If depth information and 3D image data are introduced, then objects with different volumes can be distinguished, but the network model parameters increase
Solution Approach 1:
The patent uses superpixel segmentation to divide the 3D point cloud into discrete object regions before feeding them to the neural network. This segmentation reduces the overall data volume by grouping similar 3D points together, creating a more compact representation. Instead of processing every individual 3D point, the network processes segmented regions, significantly reducing the number of parameters needed while preserving volume differentiation capability.
Solution Approach 2:
The patent efficiently handles the transition from 2D to 3D by using coordinate transformation mathematics rather than complex 3D imaging hardware. The depth calculation from binocular camera disparity maps provides 3D information through computational geometry, adding volume differentiation capability without requiring a full 3D camera system. This approach adds minimal computational overhead while enabling accurate volume measurement.
Data Source
AI summary
An object recognition method, an object recognition system, and a readable storage medium are provided. The object recognition method is to recognize an object from a first image, and includes: acquiring a first image; calculating depth information of the first image; performing superpixel segmentation on the first image, to obtain a superpixel image; generating three-dimensional image data of the first image according to the depth information and the image data of the superpixel image; and inputting the three-dimensional image data into a depth neural network for object recognition, to obtain a recognition result.