Trailer Coupler Image Labeling for Accurate Hitch Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) face challenges in accurately identifying trailer couplers due to variations in color, type, and shape, as well as environmental lighting conditions, which can lead to difficulties in coupling trailers to vehicles.
Innovation Solution
A system that includes a human-machine interface, a camera, a global positioning system, and a remote computing system, utilizing a trailer coupler labeling application with machine learning algorithms to detect and label the trailer coupler in images, improving identification accuracy and reducing computational burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If computer vision is used to detect trailer couplers, then automation is improved, but detection precision deteriorates due to variations in color, type, shape, and lighting conditions
Solution Approach 1:
The system uses color segmentation and color space transformation (RGB to HSV) to detect and adapt to different trailer coupler colors. The algorithm identifies the dominant color of the trailer coupler and adjusts detection parameters accordingly, enabling accurate detection across various color conditions that would otherwise confuse the computer vision system.
Solution Approach 2:
The system dynamically adjusts detection parameters based on environmental lighting conditions and trailer coupler characteristics. By changing parameters such as color thresholds, segmentation levels, and feature detection sensitivity, the system maintains high detection precision across diverse conditions including different lighting, colors, types, and shapes of trailer couplers.
2Measurement precision
If electronic tags or ID are placed on trailer couplers for identification, then detection precision is improved, but device complexity increases
Solution Approach 1:
The system enables trailer couplers to be detected and identified using their inherent visual characteristics (color, shape, position) without requiring any additional electronic tags, IDs, or specialized markers. The computer vision algorithm processes standard camera images to extract coupler features, allowing the existing trailer coupler structure to serve its identification function without modification.
Solution Approach 2:
The system extracts identification features directly from the visual appearance and spatial position of the trailer coupler in standard images, removing the need for separate electronic identification components. By taking out the requirement for additional hardware tags and relying solely on image processing of the coupler's natural features, the system reduces overall device complexity while maintaining identification accuracy.
3Measurement precision
If multiple sensors and processing systems are added to improve detection accuracy, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system merges multiple detection functions (color detection, edge detection, position calculation, movement analysis) into a single integrated computer vision algorithm running on the vehicle's existing controller. By combining these functions rather than using separate hardware sensors and processing units, the system achieves high location accuracy while maintaining operational simplicity through a unified software-based approach.
Solution Approach 2:
The system uses a multi-functional computer vision algorithm that performs detection, identification, positioning, and tracking of trailer couplers using a single processing pipeline. This universal approach allows the same system to handle various trailer coupler types, colors, and conditions without requiring operator intervention or complex configuration, improving both accuracy and ease of operation.
Data Source
AI summary
A system and method for locating a trailer coupler on a trailer to assist with the coupling of the trailer coupler to a vehicle hitch on a vehicle. The system includes a human-machine interface, a camera, a global positioning system, a remote computing system, a vehicle-to-infrastructure communication network, controllers, a memory, sensors, and a trailer coupler labeling application. The trailer coupler labeling application includes: sensing at least one of a hitched state and an unhitched state of the trailer coupler, capturing an optical data with the camera when the vehicle hitch is in the hitched and unhitched state, determining a distance of the trailer coupler relative to the camera when the vehicle hitch is in the hitched state, identifying the location of the trailer coupler on an image defined by the captured data and creating a label on the image of the trailer coupler.