Automated Container Counting via Digital Image Analysis
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Solution Overview
Problem
Current deposit-refund programs for beverage containers require labor-intensive manual sorting and counting by human operators to determine refund amounts, which is inefficient and time-consuming, especially when containers are collected in opaque receptacles or not visible.
Innovation Solution
A computer-implemented method using object detection and estimation algorithms, potentially aided by artificial neural networks, to detect and count beverage containers in digital images, estimating their number and monetary value, even when they are inside opaque receptacles by estimating the receptacle's dimensions and applying volumetric formulas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting and counting by human operators is used, then accuracy of container classification can be maintained, but productivity is reduced and loss of time increases
Solution Approach 1:
The patent replaces the mechanical manual sorting and counting system with an automated digital image analysis system using object detection algorithms. The system captures images of containers and uses computer vision to automatically detect, classify, and count containers by size and material type, eliminating the need for manual human operation while maintaining high accuracy through algorithmic analysis.
Solution Approach 2:
The system enables self-service automated container classification and counting without requiring human operators. The object detection algorithm independently processes images, identifies container characteristics, and generates classification results automatically, allowing the system to serve itself in performing tasks that previously required human labor.
2Measurement precision
If manual counting of containers in opaque receptacles is attempted, then measurement precision may be maintained for visible containers, but productivity drops significantly and loss of time increases
Solution Approach 1:
The patent replaces manual visual inspection and counting with automated optical detection systems. Multiple cameras capture images from different angles, and object detection algorithms process these images to identify and count containers within receptacles, dramatically reducing the time required while maintaining accurate counting through systematic image analysis.
Solution Approach 2:
The system transitions from two-dimensional manual visual inspection to three-dimensional multi-angle imaging. By capturing images from multiple perspectives and using depth information, the system can penetrate and analyze the contents of opaque receptacles more effectively, allowing operators to count containers that would be hidden from a single viewpoint.
3Productivity
If automated image analysis is implemented, then productivity increases and loss of time decreases, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional integrated system where the same digital image analysis platform performs multiple tasks: detecting container presence, classifying by size, identifying material type, and counting quantities. The object detection algorithm is designed to handle various container types and receptacle configurations universally, reducing the need for multiple specialized devices and simplifying the overall system architecture.
4Ease of operation
If containers are stored in opaque receptacles, then ease of operation for transport is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system overcomes the opacity barrier by transitioning from single-viewpoint two-dimensional inspection to multi-angle three-dimensional imaging. Cameras positioned at different heights and angles capture images that reveal container contents through geometric relationships, allowing detection of containers within opaque receptacles without requiring the receptacles to be transparent.
Data Source
AI summary
A computer-implemented method, a computer system, and a computer program product are provided for estimating output data that includes or is based on a number of containers, which may or may not be in receptacles. The method involves using an object detection algorithm operating on an input digital image to detect container images and receptacle images (if any), and using an estimation algorithm operating on the detected images to estimate the number of containers. Estimating the number of containers may involve counting the number of containers within different container classes, as determined by the object detection algorithm. Estimating the number of containers may involve estimating a size of a receptacle in a detected receptacle image based on analysis of a detected reference container image. A detected reference object image may be used to assist with classifying detected container images in different container classes.