A lightweight encoder–decoder network for automatic pavement crack detection

作者：Guijie Zhu, Jiacheng Liu, Zhun Fan, Yuan Duan, Peili Ma, Meihua Wang, Weihua Sheng, Kelvin C. P. Wang · 发表于：Computer-Aided Civil and Infrastructure Engineering · 年份：2023 · DOI：10.1111/mice.13103 · 被引用次数：166 · 研究领域：Infrastructure Maintenance and Monitoring、Asphalt Pavement Performance Evaluation、Concrete Corrosion and Durability

Cracks are the most common damage type on the pavement surface. Usually, pavement cracks, especially small cracks, are difficult to be accurately identified due to background interference. Accurate and fast automatic road crack detection play a vital role in assessing pavement conditions. Thus, this paper proposes an efficient lightweight encoder–decoder network for automatically detecting pavement cracks at the pixel level. Taking advantage of a novel encoder–decoder architecture integrating a new type of hybrid attention blocks and residual blocks (RBs), the proposed network can achieve an extremely lightweight model with more accurate detection of pavement crack pixels. An image dataset consisting of 789 images of pavement cracks acquired by a self-designed mobile robot is built and utilized to train and evaluate the proposed network. Comprehensive experiments demonstrate that the proposed network performs better than the state-of-the-art methods on the self-built dataset as well as three other public datasets (CamCrack789, Crack500, CFD, and DeepCrack237), achieving F1 scores of 94.94%, 82.95%, 95.74%, and 92.51%, respectively. Additionally, ablation studies validate the effectiveness of integrating the RBs and the proposed hybrid attention mechanisms. By introducing depth-wise separable convolutions, an even more lightweight version of the proposed network is created, which has a comparable performance and achieves the fastest inference speed with a model parameter size ...