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)
Cited by
2 articles.
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