Return Machine Tamper Detection via Mark Dimension Analysis
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Solution Overview
Problem
Conventional automatic return machines for containers are vulnerable to manipulation, allowing fraudulent activities as they rely solely on the recognition of identification marks without considering the context of the mark's size and location, leading to potential misuse.
Innovation Solution
The readout device determines the dimensions and location of the identification mark in the recognition area and compares them with expected criteria to detect manipulation, using camera-based image processing and optional separate measuring devices to verify the authenticity of the container.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the reading device only checks for the presence of an identification mark, then the device complexity is low and operation is simple, but the system becomes vulnerable to manipulation and fraud
Solution Approach 1:
The system performs preliminary verification of the identification mark's properties (dimension, position, aspect ratio) before accepting the container. By checking these parameters in advance during the verification process, the system prevents fraudulent containers from being accepted, thereby improving reliability without requiring complex post-detection measures.
Solution Approach 2:
The patent replaces simple presence detection with automated image processing and computational analysis. The reading device captures images and automatically analyzes mark dimensions, position, and aspect ratios using computer vision algorithms, substituting manual or simple mechanical verification with intelligent automated systems that enhance security while maintaining operational simplicity.
2Measurement precision
If the reading device captures high-resolution images for detailed analysis, then measurement precision improves for detecting manipulation, but the use of energy and processing time increase
Solution Approach 1:
The system uses partial action by capturing images at standard resolution and only performing detailed analysis when manipulation is suspected. The verification process first checks basic presence, then selectively applies detailed dimension and position analysis only when needed, avoiding excessive energy consumption while maintaining measurement precision when required.
Solution Approach 2:
The system dynamically adjusts image processing parameters based on verification needs. Instead of always using high-resolution capture and full analysis, the reading device adjusts its measurement precision and processing intensity according to the specific verification context, optimizing the balance between measurement accuracy and energy consumption.
3Reliability
If the system verifies multiple parameters of the identification mark (dimension, position, aspect ratio), then reliability against fraud improves, but the complexity of the verification process increases
Solution Approach 1:
The system merges multiple verification parameters (dimension check, position verification, aspect ratio analysis) into a single integrated image processing workflow. By combining these checks into one unified verification process rather than separate steps, the system maintains high reliability while preserving operational simplicity and ease of use.
Solution Approach 2:
The reading device performs self-service by automatically capturing images, analyzing multiple mark parameters, and making verification decisions without human intervention. The system independently evaluates dimension, position, and aspect ratio, then automatically accepts or rejects the container, maintaining simplicity for the user while ensuring accurate multi-parameter verification.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively differentiates between genuine and manipulated identification marks, preventing fraudulent activities by ensuring that only correctly sized and positioned marks on containers trigger payment, thereby enhancing security and integrity in the return process.
Implementation Method 1
a sensor device (20), in particular a camera device, for detecting an identification mark (5) on the container (4)
Data Source
Figure 1~2
Figure 3
AI summary
A return machine (1) for containers (4) comprises a receiving device (10) for accepting a container (4) bearing an identification mark (5) that is entered into the return machine (1) during an input process, and a reading device (2) for detecting the identification mark (5) assigned to the container (4) in a detection area (200). The reading device (2) is configured to determine a dimension (A, B) of the identification mark (5, 6) and/or a location (O) of the identification mark (5, 6) in the detection area (200) and to compare the dimension (A, B) and/or the location (O) with at least one comparison criterion in order to infer tampering from the comparison. In this way, a return machine is provided that offers additional security against tampering in a simple manner.