IEEE Transactions on Intelligent Transportation Systems· 2016Q1
Automatic Road Crack Detection Using Random Structured Forests
- 1,492citations
- Q1SCImago
- 2016year
Short summary
A new framework called CrackForest uses random structured forests and novel features to automatically detect road cracks with high precision, overcoming challenges like intensity inhomogeneity and noise.
AI-generated from the title and abstract; the full text is not read.
Key points
- CrackForest framework uses random structured forests for road crack detection.
- Integral channel features improve crack representation despite intensity variations.
- A new crack descriptor effectively differentiates cracks from similar-looking noise.
- The method is faster and easier to parallelize than competing approaches.
AI-generated from the title and abstract; the full text is not read.
Abstract
Cracks are a growing threat to road conditions and have drawn much attention to the construction of intelligent transportation systems. However, as the key part of an intelligent transportation system, automatic road crack detection has been challenged because of the intense inhomogeneity along the cracks, the topology complexity of cracks, the inference of noises with similar texture to the cracks, and so on. In this paper, we propose CrackForest, a novel road crack detection framework based on random structured forests, to address these issues. Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high-performance crack detector, which can identify arbitrarily complex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively. In addition, our method is faster and easier to parallel. Experimental results prove the state-of-the-art detection precision of CrackForest compared with competing methods.
The authors' abstract, as published at the source. IEEE Transactions on Intelligent Transportation Systems, 2016 · DOI ↗
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Field: Civil and Structural Engineering
Civil and Structural EngineeringEngineering