Optical Traffic Lane Recognition with Region-Filtered Regression
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Solution Overview
Problem
Existing methods for traffic lane recognition in vehicles struggle to accurately detect linear objects such as lane boundaries and construction markings due to the need for training regressions with contradictory target coordinates, leading to artifacts and difficulty in training.
Innovation Solution
A method using an ensemble of an image classifier and regression to identify relevant regions in an input image, where the regression is trained only on these regions, and a shared neural network is employed to enhance detection accuracy, allowing for precise lane boundary recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a regression is trained with target coordinates for all image regions including background regions, then the regression can output coordinates for the entire image, but the training becomes self-contradictory and produces artifacts
Solution Approach 1:
The image is segmented into multiple regions (e.g., grid cells), and the regression is trained only on regions that contain linear objects, excluding background regions. This segmentation allows the regression to focus on relevant areas without being confused by contradictory training signals from background regions, thereby eliminating training artifacts while maintaining coordinate detection accuracy.
Solution Approach 2:
An image classifier is introduced as an intermediary component that first identifies which regions contain linear objects before the regression processes those regions. This intermediary filter ensures that the regression only receives training data from relevant regions, resolving the self-contradiction in training by preventing background regions from generating spurious training signals.
2Reliability
If the regression is trained only on regions with linear objects, then training becomes simpler and more reliable, but the system requires an additional image classifier component
Solution Approach 1:
The image classifier performs a preliminary action by pre-identifying regions containing linear objects before the regression is applied. This preliminary classification step simplifies the regression training by ensuring it only processes relevant regions, making the overall system more reliable while the modular architecture keeps complexity manageable through clear functional separation.
3Ease of operation
If the regression outputs coordinates for background regions with zero coordinates, then all regions can be processed uniformly, but this encourages the regression to introduce artifacts to compensate for the contradictory training modes
Solution Approach 1:
Background regions are extracted and removed from the training data fed to the regression. By taking out the problematic background regions that cause self-contradiction, the system eliminates the need for the regression to handle uniform processing of all regions, thereby preventing artifact generation while maintaining processing efficiency for relevant regions only.
Data Source
AI summary
A method for detecting at least one linear object in an input image is disclosed. The input image, and/or an extract of the input image, is fed to an image classifier, which classifies specified regions of the input image or extract in each case at least into relevant regions, the center of which lies in fairly close proximity to the center point of at least one linear object passing at least partially through this region, and background regions where this is not the case. For the relevant regions, coordinates are acquired from a regression which indicate at least one local course of the linear object in the relevant regions. From these coordinates, the course of the linear object is evaluated in the entire input image.