Pavement crack detection based on local enhancement attention mechanism and deep semantic-guided multi-feature fusion

作     者：Li, Jie Li, Huanwen Tu, Jingmin Liu, Zhao Yao, Jian Li, Li 

作者机构：Hubei Univ Technol Sch Elect & Elect Engn Wuhan Peoples R China Wuhan Univ Sch Remote Sensing Informat Engn Wuhan Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen Peoples R China 

出 版 物：《JOURNAL OF ELECTRONIC IMAGING》 (J. Electron. Imaging)

年 卷 期：2024年第33卷第6期

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0702[理学-物理学] 

基　　金：National Natural Science Foundation of China Key Laboratories of Sensing and Application of Intelligent Optoelectronic System in Sichuan Provincial Universities [ZNGD2308] Shenzhen Science and Technology Program [JCYJ20230807090206013] 

主　　题：automatic crack detection semantic information local enhancement multi-scale feature fusion attention mechanism 

摘      要：Cracks, as a typical type of road defect, are important to be detected and repaired in time for transportation maintenance. However, fine-grained crack detection remains a challenge because of the slender shape, backdrop interference, and noise. We propose an automatic fine-grained crack detection algorithm based on a local enhancement self-attention mechanism and a deep semantic-guided multi-scale fusion strategy. The proposed network is built around an encoder-decoder framework. Inspired by the characteristic of global integrity and local continuity, we employ a Local Enhancement Self-Attention Block (LE Self-AB) in the encoder part. It captures the overall trend of cracks from a global perspective while focusing on the local details in the crack regions. The decoder network adopts a deep semantic-guided multi-scale fusion strategy. This strategy enables the model to benefit from both global and local information through cross-scale feature transfer and fusion. To validate the effectiveness and reliability of our approach, training and evaluation are conducted on the CrackTree260 dataset. The generalization of the model is tested on the CRKWH100 and CrackLS315 datasets. The experimental results demonstrate that the proposed method outperforms other advanced crack detection methods with an Optimal Dataset Scale of 0.919, 0.918, and 0.894 on three datasets, respectively. (c) 2024 SPIE and IS&T