DCNAM: Automatic detection of pixel level fine crack using a densely connected network with attention mechanism

Abstract

Deep-learning-based crack identification has emerged as a prominent research area in structural health monitoring. Although the detection of common cracks has been the predominant focus in previous studies, the identification of tiny cracks has often been neglected. Efficiently managing thin cracks is vital, because they can threaten the overall structural integrity over time if left unaddressed. We address this gap by targeting thin cracks within a broad category of crack types. We introduce a fine-crack-detection algorithm that efficiently detects both common and tiny cracks. Owing to the limited availability of publicly accessible datasets specifically focused on thin cracks, we collect images of fine cracks to train and evaluate our algorithm. To validate the efficiency of our method, we conduct experiments on three publicly available crack datasets and our private dataset. Compared with the baseline neural network, our proposed approach demonstrates superior performance across all evaluation metrics. Furthermore, our model exhibits impressive generalization ability across the datasets, with the F1 score and mean intersection over union improving by 22.42% and 28.07%, respectively. Notably, our observations indicate that the advantages of the proposed method become more pronounced as the dataset size increases.