Vehicle Obstacle Detection Using Active Deformable Contour Model
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Solution Overview
Problem
Existing object detection methods for vehicles face challenges with high false recognition rates, lengthy processing times, and integration difficulties with existing systems, especially when recognizing obstacles in dynamic environments, leading to inefficiencies and increased costs.
Innovation Solution
An object detection device embedded in vehicles uses a Poisson gradient vector flow based active deformable contour model and a multi-mesh algorithm with different grid sizes to accurately extract obstacle contours and recognize types, enhancing computation speed and integration feasibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image comparison methods are used to recognize obstacles by dividing and comparing extracted portions, then object type classification can be performed, but false recognition rate increases and search time lengthens when extracted portions are too small
Solution Approach 1:
The patent divides the image processing task into two stages: first extracting the complete obstacle contour using active deformable contour model, then dividing the extracted obstacle into multiple portions for classification. This ensures that the initial extraction captures the complete obstacle shape before segmentation for type recognition, preventing false recognition due to incomplete portions.
Solution Approach 2:
The patent performs preliminary extraction of the complete obstacle contour using active deformable contour model before进行分类. By establishing the complete boundary first, subsequent division into portions for classification can be performed with confidence that each portion represents a valid part of the complete obstacle, reducing false recognition.
2Measurement precision
If calculation of image characteristic values and logical mechanisms are performed on each divided portion to determine object types, then classification can be achieved, but processing time increases and integration with embedded systems becomes difficult
Solution Approach 1:
The patent extracts only the essential feature - the complete obstacle contour - using active deformable contour model, then divides this extracted contour into portions for classification. This extraction approach focuses computation on the boundary representation rather than processing entire images or multiple feature sets, reducing processing time while maintaining classification accuracy.
Solution Approach 2:
The patent uses active deformable contour model to create an accurate boundary copy of the obstacle, then performs classification on this contour representation rather than on the original full-resolution image data. This copying approach maintains classification accuracy while significantly reducing computational load and processing time.
3Reliability
If millimeter wave radar is equipped on vehicles for obstacle location, then long measuring distance and stable operation are achieved, but equipment cost and operation cost increase
Solution Approach 1:
The patent replaces the mechanical millimeter wave radar system with an optical/image processing system using active deformable contour model. By substituting radar hardware with software-based image analysis, the system achieves comparable or superior performance in urban environments while eliminating the high equipment and operational costs associated with radar systems.
Solution Approach 2:
The patent uses standard image cameras and processing algorithms as a cheaper alternative to expensive millimeter wave radar. The image-based approach using active deformable contour model provides cost-effective obstacle detection suitable for urban environments where radar's long-range capability is less critical than in rural settings.
4Quantity of substance
If dynamic extraction method is used to extract obstacles without additional equipment, then equipment cost is reduced, but calculation amount increases and processing time lengthens
Solution Approach 1:
The patent changes the parameter representation from full image data to contour boundary data extracted by active deformable contour model. By transforming the problem from processing entire images to processing extracted contours, the system maintains low equipment requirements while significantly reducing computational load and improving processing speed.
Solution Approach 2:
The patent segments the image processing task into contour extraction followed by classification, using active deformable contour model to isolate the obstacle boundary. This segmentation allows subsequent processing to focus only on the relevant obstacle regions rather than entire images, improving processing efficiency without requiring additional expensive equipment.
Data Source
AI summary
An object detection method with a rising classifier effect is embedded in an object detection device and has steps of acquiring image information; determining a position of an obstacle in the image information, wherein the image information is detected for generation of an extracted range corresponding to the obstacle; recognizing an extracted contour of the obstacle, wherein the extracted contour of the obstacle associated with the extracted range is obtained by using an algorithm for Poisson gradient vector flow based active deformable contour model and a multi-mesh algorithm with different grid sizes; and forming a maximum border of the extracted contour of the obstacle for a classifier to recognize a type of the obstacle according to the maximum border of the obstacle. Accordingly, the object detection method is advantageous in complete extraction, higher recognition rate, faster computation and feasibility to be integrated with vehicle systems.