Neural Network Object Structure Detection via Edge Endpoints
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Solution Overview
Problem
Existing object recognition systems struggle to accurately detect and identify object structures of arbitrary shapes in complex environments, such as urban scenarios, where lane edges and traffic islands can have polyline shapes.
Innovation Solution
A method utilizing an artificial neural network trained to determine feature data comprising basic geometric shapes and colors, which generates feature maps and employs edge endpoint detectors and graph intermediate point determiners to reconstruct object structures from image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional methods such as bounding boxes or line recognition are used, then the system can recognize objects of fixed shapes, but it fails to detect objects of arbitrary shapes like polyline shapes in urban scenarios
Solution Approach 1:
The patent segments the object detection problem into two independent tasks: detecting edge endpoints (start and end points of line segments) and detecting intermediate graph points (points along the line segments). This segmentation allows the system to handle arbitrary shapes by composing them from multiple line segments connecting these points, rather than attempting to detect complete objects of fixed shapes as a whole.
Solution Approach 2:
The patent transitions from traditional 2D bounding box detection to a graph-based representation where objects are described by sequences of points (edge endpoints and intermediate points) connected by line segments. This dimensional transformation enables the system to represent arbitrary polyline shapes by specifying coordinates of multiple points rather than constraining objects to fixed geometric forms.
2Measurement precision
If object recognition systems are designed for specific scenarios like highway lane detection, then they can achieve high accuracy for that specific task, but they fail in other scenarios such as urban environments with arbitrary shaped objects
Solution Approach 1:
The patent creates a universal object detection framework that can handle multiple scenarios (highway lane detection, urban traffic islands, road edges) through a single graph-based approach. The system detects edge endpoints and intermediate points that can represent any polyline shape, making it applicable to various objects across different environments without requiring scenario-specific customization.
Solution Approach 2:
The patent changes the fundamental parameters of object representation from fixed shape constraints (bounding boxes, predefined line patterns) to flexible point-based coordinates (edge endpoints and intermediate graph points). This parameter transformation allows the system to adapt to different scenarios by varying the number and positions of detected points while maintaining the same detection methodology.
3Measurement precision
If the system attempts to provide detailed object structure information for arbitrary shapes, then recognition accuracy improves, but the complexity of the detection system increases
Solution Approach 1:
The patent divides the complex task of detecting arbitrary shaped objects into two simpler sub-tasks performed by separate neural network heads: an edge endpoint detector head that identifies start and end points of line segments, and an intermediate graph point detector head that identifies points along the segments. This segmentation reduces the complexity of each individual detection task while providing comprehensive object structure information through the combination of both detectors.
Data Source
AI summary
The disclosure relates to determining an object structure of an object, such as lane edges and/or lane markings. Image data describing an image of an environment with the object located therein are provided and then fed into at least one neural network that is trained to determine feature data, including predetermined features with respect to geometric shapes and/or colors of the object. Edge endpoints of the object structure of the object are determined by applying an edge endpoint determiner, and then an ROI operation is performed on at least one surrounding area around at least one determined edge endpoint. At the same time, intermediate graph points of the object structure are determined by applying an intermediate graph point determiner. The final object structure is determined and/or marked based on the determined edge endpoints and intermediate graph points. The determined object structure is provided to a computer vision functionality.