Railway Obstacle Detection Using Multi-Band Imaging and Rail Tracking
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Solution Overview
Problem
Existing Electro-Optic (EO) computer aided imaging techniques face challenges in accurately detecting and classifying objects and obstacles in railway scenes under diverse weather conditions and varying ranges, which are crucial for timely collision avoidance.
Innovation Solution
A method and system utilizing a combination of Long Wave Infra Red (LWIR) and visible band imagers, along with geographical information, to perform rail detection and tracking, object and obstacle detection, and classification, incorporating modules for rail detection and tracking, object and obstacle detection, classification, and display with alarm functions, and employing neural net computing for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple imagers with different sensing ranges and wavelengths are used, then detection reliability and accuracy are improved, but device complexity increases
Solution Approach 1:
The detection system is segmented into multiple specialized imagers: a first imager for short-range detection and a second imager for long-range detection. Each imager is optimized for its specific range, allowing the system to maintain high reliability across all distances without requiring a single complex imager to handle all ranges effectively.
Solution Approach 2:
The system extends detection capability into the thermal infrared wavelength dimension by incorporating imagers that operate in different spectral bands. This dimensional addition allows detection of objects based on thermal signatures, providing another layer of detection reliability beyond visible light imaging.
2Measurement precision
If multiple imagers with different fields of view are used, then object detection accuracy is improved, but device complexity increases
Solution Approach 1:
The field of view is segmented between two imagers: a first imager with a wider field of view for detecting objects at shorter ranges and a second imager with a narrower field of view for precise long-range detection. This segmentation allows each imager to be optimized for its specific angular coverage, improving overall detection accuracy.
Solution Approach 2:
Different regions of the detection space are assigned different imaging qualities: the first imager provides broader coverage with appropriate resolution for near-field objects, while the second imager provides higher angular resolution for far-field objects. Each imager's field of view and resolution are locally optimized for its operational range.
3Measurement precision
If rail-based ranging and 3D modeling are performed, then obstacle classification accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary rail detection and tracking to establish a reference rail map before obstacle detection. By pre-establishing the rail geometry and position, the system creates a stable reference framework that accelerates subsequent obstacle classification and ranging operations, reducing overall processing time.
Solution Approach 2:
The rail map serves as an intermediary data structure that mediates between raw imager data and obstacle classification results. By maintaining a separate, updated representation of rail geometry, the system avoids repeatedly processing raw images for each obstacle detection task, significantly reducing processing time while maintaining accuracy.
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
Enables robust and timely detection and classification of objects and obstacles, reducing driver workload and improving collision avoidance capabilities in both driver-operated and autonomous trains.
Implementation Method 1
A method and system utilizing a combination of Long Wave Infra Red (LWIR) and visible band imagers
Implementation Method 2
A method and system utilizing a combination of Long Wave Infra Red (LWIR) and visible band imagers
Data Source
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AI summary
A system for detection and identification of objects and obstacles near, between or on railway comprise several forward-looking imagers adapted to cover each different range forward and preferably to be sensitive each to different wavelength of radiation, including visible light, LWIR, and SWIR. The substantially homogeneous temperature along the rail the image of which is included in an imager frame assists in identifying and distinguishing the rail from the background. Image processing is applied to define living creature in the image frame and to distinguish from a man-made object based on temperature of the body. Electro optic sensors (e.g. thermal infrared imaging sensor and visible band imaging sensor) are used to survey and monitor railway scenes in real time.