TY - JOUR
T1 - DCMA-Net
T2 - A dual channel multi-scale feature attention network for crack image segmentation
AU - Yan, Yidan
AU - Sun, Junding
AU - Zhang, Hongyuan
AU - Tang, Chaosheng
AU - Wu, Xiaosheng
AU - Wang, Shuihua
AU - Zhang, Yudong
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/5/15
Y1 - 2025/5/15
N2 - Cracks are a common structural damage to pavement that significantly threatens traffic safety, so regular inspections of road conditions are essential for maintaining traffic safety. Artificial intelligence (AI) technology has demonstrated significant potential in various computer vision tasks and has been widely applied to the field of crack detection in recent years. However, the crack detection remains a challenging task due to the intricacy of crack types, the presence of intensity inhomogeneities, and the difficulty in detecting edge areas within a complex background. To address these challenges, this paper proposes a novel dual channel multi-scale feature attention segmentation network (DCMA-Net) model for crack detection. Specifically, this two feature extraction channels are exploited to obtain global and local information. A multi-scale feature extraction module (MFE) is integrated into the residual structure to capture depth multi-scale information. Additionally, a hybrid attention mechanism is employed to connect the coder and decoder, allowing the network to focus on the target area from different scales and dimensions to enrich crack feature representation. To verify the superiority of the proposed method, we evaluate it on three crack datasets and compare it with state-of-the-art crack detection techniques. The experimental results demonstrate the superior segmentation performance of our proposed network compared to existing advanced methods.
AB - Cracks are a common structural damage to pavement that significantly threatens traffic safety, so regular inspections of road conditions are essential for maintaining traffic safety. Artificial intelligence (AI) technology has demonstrated significant potential in various computer vision tasks and has been widely applied to the field of crack detection in recent years. However, the crack detection remains a challenging task due to the intricacy of crack types, the presence of intensity inhomogeneities, and the difficulty in detecting edge areas within a complex background. To address these challenges, this paper proposes a novel dual channel multi-scale feature attention segmentation network (DCMA-Net) model for crack detection. Specifically, this two feature extraction channels are exploited to obtain global and local information. A multi-scale feature extraction module (MFE) is integrated into the residual structure to capture depth multi-scale information. Additionally, a hybrid attention mechanism is employed to connect the coder and decoder, allowing the network to focus on the target area from different scales and dimensions to enrich crack feature representation. To verify the superiority of the proposed method, we evaluate it on three crack datasets and compare it with state-of-the-art crack detection techniques. The experimental results demonstrate the superior segmentation performance of our proposed network compared to existing advanced methods.
KW - Application of artificial intelligence
KW - Convolutional neural network
KW - Deep learning
KW - Multi-scale attention
KW - Pavement crack segmentation
UR - http://www.scopus.com/inward/record.url?scp=85218894271&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2025.110411
DO - 10.1016/j.engappai.2025.110411
M3 - Article
AN - SCOPUS:85218894271
SN - 0952-1976
VL - 148
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 110411
ER -