MSK-UNET: A Modified U-Net Architecture Based on Selective Kernel with Multi-Scale Input for Pavement Crack Detection

计算机科学 核(代数) 卷积(计算机科学) 背景(考古学) 编码器 算法 棱锥(几何) 人工智能 数学 人工神经网络 古生物学 几何学 生物 操作系统 组合数学
作者
Xiaoliang Jiang,Jinyun Jiang,Jianping Yu,Jun Wang,Ban Wang
出处
期刊:Journal of Circuits, Systems, and Computers [World Scientific]
卷期号:32 (01) 被引量:3
标识
DOI:10.1142/s0218126623500068
摘要

Pavement crack condition is a vitally important indicator for road maintenance and driving safety. However, due to the interference of complex environment, such as illumination, shadow and noise, the automatic crack detection solution cannot meet the requirements of accuracy and efficiency. In this paper, we present an extended version of U-Net framework, named MSK-UNet, for pavement crack to solve these challenging problems. Specifically, first, the U-shaped network structure is chosen as the framework to extract more hierarchical representation. Second, we introduce selective kernel (SK) units to replace U-Net’s standard convolution blocks for obtaining the receptive fields with distinct scales. Third, multi-scale input layer establishes an image pyramid to retain more image context information at the encoder stage. Finally, a hybrid loss function including generalized Dice loss with Focal loss is employed. In addition, a regularization term is defined to reduce the impact of imbalance between positive and negative samples. To evaluate the performance of our algorithm, some tests were conducted on DeepCrack dataset, AsphaltCrack300 dataset and Crack500 dataset. Experimental results show that our approach can detect various crack types with diverse conditions, obtains a better performance in precision, recall and [Formula: see text]-score, with 97.43%, 96.95% and 97.01% precision values, 82.51%, 93.33% and 87.58% recall values and 95.33%, 99.24% and 98.55% [Formula: see text]-score values, respectively.
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