Lane Instance Segmentation from Semantic Masks and Polylines
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Solution Overview
Problem
Constructing instance segmentation databases for lane detection in vehicles is costly and time-consuming, and existing lane detection systems face challenges with errors due to camera malfunctions or environmental conditions, necessitating high-performance lane detection software that is not efficiently developed.
Innovation Solution
A lane data construction apparatus and method that extracts feature points from lane images using semantic segmentation data, generates polylines connecting these points, and assigns instance IDs to create instance segmentation data, reducing costs and time by constructing an instance segmentation database efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If instance segmentation database is constructed using traditional manual methods, then data accuracy is improved, but construction time and cost increase significantly
Solution Approach 1:
The system performs preliminary semantic segmentation to generate lane masks before extracting feature points and generating polylines. This preliminary classification of road regions into semantic categories (lane, sidewalk, building, etc.) prepares the data structure in advance, enabling automated instance segmentation without manual annotation while maintaining accuracy.
Solution Approach 2:
The system generates synthetic instance segmentation data by copying and transforming semantic segmentation results through automated processing pipelines. Instead of manually creating each annotation, the system replicates the semantic structure into instance-level polylines and masks through algorithmic transformation, dramatically reducing construction time while preserving data fidelity.
2Measurement precision
If instance segmentation database is constructed using traditional manual methods, then data accuracy is improved, but construction cost increases significantly
Solution Approach 1:
The system performs self-service by automatically generating instance segmentation data from semantic segmentation inputs without requiring manual annotation. The automated pipeline extracts feature points, generates polylines, creates masks, and assigns instance IDs autonomously, eliminating labor costs while maintaining data quality through algorithmic precision.
Solution Approach 2:
The system replaces the mechanical manual annotation process with an automated computational pipeline. Instead of human annotators manually drawing polylines and creating masks, the system uses image processing algorithms, feature detection, and polynomial curve fitting to automatically generate instance segmentation data, significantly reducing construction costs.
3Device complexity
If camera-based lane detection is used, then system simplicity is maintained, but reliability decreases under adverse conditions
Solution Approach 1:
The system uses composite data representation by combining semantic segmentation masks, feature points, and polyline structures to create robust instance segmentation data. This multi-component approach integrates different types of information (semantic categories, geometric features, boundary definitions) to improve detection reliability while maintaining system simplicity through unified processing.
Data Source
AI summary
A lane data construction apparatus includes a processor that generates a lane image based on semantic segmentation data including semantic information for a lane, generates a plurality of polylines using a plurality of feature points extracted from the lane image, and generates instance segmentation data including instance information related to each lane based on the plurality of polylines and a memory operatively connected to the processor and storing data and an algorithm run by the processor.