Towbar Detection Using Reference Image Comparison and License Plate Recognition
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Solution Overview
Problem
Automatic vehicle detection systems face precision issues in differentiating between vehicles towing trailers and those following closely, leading to erroneous detections and increased operational costs due to false alarms, and existing methods for detecting drawbars are complex and expensive.
Innovation Solution
A method using a single control camera to capture images of a control zone, comparing them to a reference image, and recognizing license plates to determine the presence of a drawbar by identifying non-matching plates, implemented with a varifocal sensor and periodic image capture, reducing computational intensity and equipment costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single control camera is used to detect drawbars, then equipment cost and complexity are reduced, but detection precision and reliability deteriorate due to difficulty in differentiating between towed vehicles and closely following vehicles
Solution Approach 1:
The detection process is segmented into multiple independent techniques: (1) comparing control zone images to reference images to detect drawbar presence, (2) recognizing license plates on both vehicles to verify towing relationship, and (3) using learning techniques for drawbar recognition. This segmentation allows the system to achieve high detection precision through multiple verification steps while maintaining relatively simple equipment by using a single camera.
Solution Approach 2:
The system performs preliminary actions by capturing reference images of the control zone when no vehicles are present, and pre-identifying license plates before making the final drawbar detection determination. This preliminary preparation enables more accurate comparison and reduces false detections during actual vehicle passage.
2Productivity
If automated detection systems are used without human intervention, then operating costs are reduced, but detection precision deteriorates leading to erroneous detections and false alarms
Solution Approach 1:
The system implements feedback mechanisms by comparing detected drawbars against multiple criteria: image comparison results, license plate matching verification, and learning technique outcomes. This multi-layered feedback validation ensures high detection reliability while maintaining automated operation, reducing false alarms without requiring human intervention.
Solution Approach 2:
The system applies partial action by selectively implementing detection techniques based on confidence levels. When image comparison and license plate verification provide sufficient evidence, the system makes automated determinations without requiring all possible verification steps, thus maintaining both automation and reliability.
3Reliability
If multiple detection techniques are implemented including image comparison and license plate recognition, then detection reliability is improved, but computational intensity and processing time increase
Solution Approach 1:
The system implements detection techniques in a staged manner, performing image comparison first as a quick filter, then applying license plate recognition only when needed for verification. Learning techniques are applied selectively based on initial detection results. This partial application of techniques reduces processing time while maintaining high reliability through targeted use of computationally intensive methods.
Data Source
Figure 1~3
Figure 2
AI summary
The invention relates to a method for detecting a towbar (105) connecting two motor vehicles (20, 25) moving between a first zone (55) and a second zone (60), the method comprising steps of: acquiring a single reference image of the control area (65) by means of the control camera (45), no vehicles (20, 25) being present in the control area (65) during the acquisition step; determining the potential presence of a towbar (105) by implementing at least one technique selected from: a first technique comprising the steps of comparing each working image to the reference image, and a second technique comprising steps of identifying a registration plate at the front of the first vehicle (20) and at the rear of the second vehicle (25).