Work

FD2-YOLO: A Frequency-Domain Dual-Stream Network Based on YOLO for Crack Detection

Year: 2025

Type: article

Abstract: Crack detection in cement infrastructure is imperative to ensure its structural integrity and public safety. However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is often ineffective in detecting slender or variable cracks in complex scenarios. We propose a novel network, FD2-YOLO, based on frequency-domain dual-stream YOLO, for accurate and efficient detection of cement cracks. Firstly, the model employs a dual backbone architecture, integrating edge and texture features in the frequency domain with semantic features in the spatial domain, to enhance the extraction of crack-related features. Furthermore, the Dynamic Inter-Domain Feature Fusion module (DIFF) is introduced, which uses large-kernel deep convolution and Hadamard to enable the adaptive fusion of features from different domains, thus addressing the problem of difficult feature fusion due to domain differences. Finally, the DIA-Head module has been proposed, which dynamically focuses on the texture and geometric deformation features of cracks by introducing the Deformable Interactive Attention Module (DIA Module) in Decoupled Head and utilizing its Deformable Interactive Attention. Extensive experiments on the RDD2022 dataset demonstrate that FD2-YOLO achieves state-of-the-art performance. Compared with existing YOLO-based models, it improves mAP50 by 1.3%, mAP50-95 by 1.1%, recall by 1.8%, and precision by 0.5%, validating its effectiveness in real-world object detection scenarios. In addition, evaluation on the UAV-PDD2023 dataset further confirms the robustness and generalization of our approach, where FD2-YOLO achieves a mAP50 of 67.9%, mAP50-95 of 35.9%, recall of 61.2%, and precision of 75.9%, consistently outperforming existing lightweight and Transformer-based detectors under more complex aerial imaging conditions.

Source: Sensors

DOI: 10.3390/s25113427

Authors: Junwen Zhu, Jinbao Sheng, Qian Cai

Institutions: Nanjing Hydraulic Research Institute, Ministry of Water Resources of the People's Republic of China

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137