Arrival-Departure Image Comparison for Vehicle Fluid Leak Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle sensor systems often fail to promptly detect fluid leaks, leading to potential extensive damage before the issue is recognized, resulting in costly repairs.
Innovation Solution
A system utilizing a forward-facing camera and inertial measurement unit to capture and compare arrival and departure images of a parking spot, employing image stabilization and machine learning algorithms to detect anomalies indicative of fluid leaks, with alerts generated for the driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle sensors are used to detect fluid leaks, then leak detection capability is provided, but detection timing is delayed causing extensive damage before recognition
Solution Approach 1:
The system captures images of the parking spot before the vehicle departs (arrival images) and compares them with departure images to identify fluid leaks. This preliminary capture and comparison of images enables detection before the vehicle moves away, significantly reducing detection timing delay and allowing early intervention to prevent extensive damage.
Solution Approach 2:
The system creates a visual copy (image) of the parking spot condition at arrival and compares it with the departure condition. This copying approach allows precise detection of fluid leaks by comparing the two states, improving both detection capability and timing without requiring continuous sensor monitoring during the parking period.
2Measurement precision
If machine learning algorithms are used for automatic leak detection, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the critical difference between arrival and departure images to identify fluid leaks, rather than analyzing entire images or using complex continuous monitoring. This extraction of essential changes reduces computational complexity while maintaining high detection accuracy through focused comparison of key temporal states.
Solution Approach 2:
The system performs image comparison only at specific moments (arrival and departure) rather than continuous monitoring, and uses machine learning algorithms only for the critical task of identifying differences between these two states. This partial application of complex algorithms reduces overall computational burden while maintaining high detection accuracy when needed.
Data Source
AI summary
A vehicle controller receives images from a camera upon arrival and upon departure. A location of the vehicle may be tracked and images captured by the camera may be tagged with a location. A departure image may be compared to an arrival image captured closest to the same location as the arrival image. A residual image based on a difference between the arrival and departure images is evaluated for anomalies. Attributes of the anomaly such as texture, color, and the like are determined and the anomaly is classified based on the attributes. If the classification indicates an automotive fluid, then an alert is generated. A machine learning algorithm for generating classifications from image data may be trained using arrival and departure images obtained by rendering of a three-dimensional model or by adding simulated fluid leaks to two-dimensional images.