Rut Detection Using Pixel-Wise Semantic Segmentation
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Solution Overview
Problem
Existing technologies face challenges in automatically detecting ruts in road infrastructure, as ruts are subtle visual differences that are difficult to distinguish from other road features using conventional image classification methods.
Innovation Solution
A computer-implemented method for rut detection using neural network-based, pixel-wise semantic segmentation with context information, which distinguishes rut pixels from non-rut pixels in road-scene images, providing pixel-wise probability values for rut detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image classification methods are used for rut detection, then the system is simple to implement, but the detection accuracy is insufficient due to subtle visual differences of ruts from other road features
Solution Approach 1:
The patent applies segmentation by dividing the image classification task into pixel-wise semantic segmentation. Instead of classifying the entire image as containing ruts or not, the system segments each pixel to determine whether it belongs to rut, road, or other features. This is achieved through a neural network that outputs probability maps for each pixel category, enabling precise localization and identification of rut regions even when they have subtle visual differences from surrounding road surfaces.
Solution Approach 2:
The patent transitions from traditional 2D image classification to pixel-wise 3D probability space by introducing depth in the probability dimension. For each pixel, the system generates probability values across multiple categories (rut, road, other features), creating a three-dimensional probability space that allows more nuanced discrimination between similar-looking road features and actual ruts, thereby improving detection accuracy.
2Measurement precision
If pixel-wise semantic segmentation is used to improve rut detection accuracy, then detection precision increases, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by performing pixel-wise semantic segmentation and generating probability maps for all pixels simultaneously through a trained neural network, rather than processing each potential rut region sequentially. The network is pre-trained on road imagery to recognize patterns of ruts, roads, and other features, enabling it to quickly classify pixels during inference. This parallel processing approach reduces computational time compared to traditional methods that might require multiple sequential analysis steps.
Data Source
AI summary
A computer-implemented method for rut detection is provided. The method includes detecting, by a rut detection system, areas in a road-scene image that include ruts with pixel-wise probability values, wherein a higher value indicates a better chance of being a rut. The method further includes performing at least one of rut repair and vehicle rut avoidance responsive to the pixel-wise probability values. The detecting step includes performing neural network-based, pixel-wise semantic segmentation with context information on the road-scene image to distinguish rut pixels from non-rut pixels on a road depicted in the road-scene image.