Instance Segmentation Imaging for Real-Time Lane Polyline Detection
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Solution Overview
Problem
Traditional methods for identifying pixels in an image, particularly for lane detection, are inefficient due to a three-step template that separates the learning objective from the inference of lane structure, leading to resource-intensive clustering and limited frame rates.
Innovation Solution
A method and system for instance segmentation in images using iterative processing to identify pixels and form polylines, where a processor identifies a starting pixel, iteratively finds adjacent row pixels, and forms lines through these pixels to represent objects, such as lanes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional three-step template methods are used for lane detection, then the system can identify pixels and cluster them into lanes, but the processing is resource intensive and frame rate is limited
Solution Approach 1:
The patent segments the lane detection process into iterative pixel identification steps rather than bulk clustering. The system processes pixels row-by-row through iterative application of the trained classifier, identifying lane pixels sequentially and constructing lane segments incrementally. This segmentation eliminates the resource-intensive clustering step while maintaining detection accuracy.
Solution Approach 2:
The trained classifier is pre-trained on labeled training data to learn lane pixel characteristics before deployment. During operation, the pre-trained classifier directly evaluates pixels without requiring post-processing clustering. This preliminary training action enables the system to make immediate detection decisions, improving frame rate while reducing runtime computational load.
2Reliability
If traditional clustering methods are used to group pixels into lanes, then lane structure can be inferred, but the process is resource intensive and slows down processing
Solution Approach 1:
The patent extracts the lane structure inference capability directly into the training phase. The trained classifier learns to identify lane pixels and their spatial relationships during training, eliminating the need for separate clustering operations. This extraction of the inference function from the runtime process reduces device complexity while preserving reliable lane structure detection.
Solution Approach 2:
The patent replaces the mechanical clustering process with a learned classification system. Instead of computationally intensive pixel clustering algorithms, the system uses a neural network classifier that has learned lane patterns during training. This substitution of mechanical processing with learned intelligence reduces computational complexity while maintaining or improving detection reliability.
3Ease of manufacture
If models are configured to use only pre-labeled training data, then training is straightforward, but the model cannot translate information to unlabeled data in new environments
Solution Approach 1:
The patent enables parameter adaptation by allowing the trained classifier to adjust its decision thresholds and probability criteria when applied to new environments. The system can modify classification parameters such as probability thresholds and spatial constraints to adapt to different road conditions, lighting, and camera perspectives while maintaining the core trained model structure.
Solution Approach 2:
The patent introduces dynamic adaptation capabilities that allow the model to adjust to new environments during operation. The system can dynamically modify classification parameters, probability thresholds, and spatial constraints based on environmental conditions. This dynamic behavior enables the model to translate learned patterns to unlabeled data in new environments while maintaining ease of initial training.
Data Source
AI summary
A method of instance segmentation in an image and a system for instance segmentation of images. The method includes identifying, with a processor, a starting pixel associated with an object in an image, the image having a plurality of rows of pixels, the starting pixel located in a row of the plurality of rows; identifying, with the processor, at least one pixel located in an adjacent row to the row in which the starting pixel is located, the at least one pixel being part of the same object as the starting pixel; iterating the previous two identification steps using the at least one identified adjacent row pixel as a start pixel for the next iteration; and connecting, with the processor, the at least one identified adjacent row pixels to form polylines representing the object.