Details

Autor(en) / Beteiligte

• Shi, Yong
• Cui, Limeng
• Qi, Zhiquan
• Meng, Fan
• Chen, Zhensong

Titel

Automatic Road Crack Detection Using Random Structured Forests

Ist Teil von

• IEEE transactions on intelligent transportation systems, 2016-12, Vol.17 (12), p.3434-3445

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2016

Quelle

IEEE

Beschreibungen/Notizen

• 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.