CrackTinyNet: A novel deep learning model specifically designed for superior performance in tiny road surface crack detection

Author:

Li Haitao1ORCID,Peng Tao12,Qiao Ningguo1,Guan Zhiwei3,Feng Xinyun1,Guo Peng4,Duan Tingting3,Gong Jinfeng4

Affiliation:

1. College of Automobile and Transportation Tianjin University of Technology and Education Tianjin China

2. Shanghai Artificial Intelligence Laboratory Shanghai China

3. Automobile and Rail Transportation College Tianjin Sino‐German University of Applied Sciences Tianjin China

4. China Automotive Technology and Research Center Co., Ltd. Tianjin China

Abstract

AbstractWith the rapid advancement of highway construction, the maintenance of highway infrastructure has become particularly vital. During highway maintenance, the effective detection of tiny road surface cracks helps to extend the lifespan of roads and enhance traffic efficiency and safety. To elevate the performance of existing road detection models, the CrackTinyNet (CrTNet) algorithm is specifically proposed for detecting tiny road surface cracks. This algorithm utilizes the novel BiFormer general visual transformer, designed expressly for tiny objects, and optimizes the loss function to a normalized Wasserstein distance loss function. It replaces traditional downsampling with Space‐to‐Depth Conv to prevent the excessive loss of tiny object information in the network structure. To highlight the model's advantage in detecting tiny road cracks, ablation experiments and comparison trials were conducted with mainstream deep learning models for crack detection. The results of the ablation experiments show that, compared to the baseline, CrTNet improved the Mean Average Precision (MAP) by 0.22. When compared to other network models suitable for road detection, these results exhibited an improvement of over 8.9%. In conclusion, the CrTNet proposed in this study enables a more accurate detection of tiny road cracks, playing a significant role in the advancement of intelligent traffic management.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Reference59 articles.

1. Pavement maintenance considering traffic accident costs

2. Anand S. Gupta S. Darbari V. Kohli S.:Crack‐pot: Autonomous road crack and pothole detection. In:2018 Digital Image Computing: Techniques and Applications (DICTA).Canberra ACT pp.1–6(2018)

3. Ministerio de Fomento de Espana.http://www.fomento.gob.es. Accessed 8 Mar 2023

4. ERF: European Road Statistics 2010.http://www.erf.be/images/...n_Road_Statistics_2010.pdf. Accessed 11 Feb 2023

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