Drone-Based Track Section Occupancy Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current track vacancy detection systems, such as axle counters, can fail to accurately determine the occupancy state of a track section, leading to errors where a section may be incorrectly marked as occupied when it is vacant, requiring manual visual verification before a reset procedure can be initiated, which is inefficient and not fully automated.
Innovation Solution
A system and method utilizing a drone system to automatically acquire aerial images of a track section, employing pattern recognition techniques to determine the presence or absence of a guided vehicle, and communicate this information to a control device for resetting the occupancy state, allowing for autonomous and efficient verification and initialization of track vacancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual visual verification by driver is used to determine track vacancy before reset, then safety is maintained through human inspection, but automation is reduced and time consumption increases
Solution Approach 1:
The patent replaces the manual visual inspection method (mechanical human operation) with an automated image recognition system using cameras and pattern recognition algorithms. The system captures images of the track section, processes them through pattern recognition to detect vehicles, and automatically determines occupancy status, thereby substituting human mechanical inspection with an automated optical recognition system.
Solution Approach 2:
The system enables the track occupancy verification process to be self-sufficient without human intervention. The image capture device automatically acquires images, the pattern recognition system autonomously analyzes them to detect vehicles, and the control device automatically determines occupancy status and authorizes reset procedures, creating a self-service automated verification system.
2Productivity
If automated image recognition system is implemented, then automation and efficiency are improved, but system complexity increases
Solution Approach 1:
The system employs a multi-functional integrated approach where a single control device coordinates multiple functions: managing the image capture device, processing images through pattern recognition, determining occupancy status, and authorizing reset procedures. This universal control architecture consolidates multiple functions into one system, improving efficiency while managing complexity through centralized coordination rather than multiple separate systems.
Data Source
Figure 1
Figure 2
AI summary
The present invention concerns a method and system for resetting a track section occupancy state, the system (1) comprising: - a control device (3) configured for cooperating with a physical detection system (22) in charge of a determination of the occupancy state for said track section (S2), wherein said control device (3) is configured for automatically sending a set of data to a drone system (4) at reception of a request for resetting the occupancy state of the track section (S2), wherein said set of data comprises at least one data enabling a drone system to acquire aerial images of the track section, the control device (3) being further configured for automatically resetting the occupancy state of the track section in case of reception of a drone system message whose occupancy state data indicates an absence of guided vehicle on said track section; - the drone system (4), comprising a camera system, and configured for automatically launching (203) a process of track section aerial image acquisition, wherein aerial images of the track section (S2) are acquired and processed in real time by the drone system (4) in order to determine whether a guided vehicle is located on the track section, the drone system (4) being further configured for automatically sending (206) a message to the control device (3), wherein said message comprises an occupancy state data for the track section (S2).