YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection

Researchers have developed YOLO-AMC, an improved YOLO architecture integrating attention mechanisms for building crack detection, achieving mAP@0.5 of 0.9917 on test datasets. The model outperforms baseline YOLOv11n and YOLOv8n while maintaining 110.95 FPS on an NVIDIA RTX 4090 and approximately 5 FPS on a Raspberry Pi 5 edge device. The open-source implementation, available on GitHub, addresses challenges in detecting thin, low-contrast cracks affected by background noise in infrastructure inspection.

arXiv:2606.12958v1 Announce Type: new Abstract: Crack detection plays an important role in infrastructure inspection and Structural Health Monitoring SHM . However, cracks typically appear as thin, low-contrast structures and are easily affected by background noise, posing challenges for existing object detection models. This study proposes an improved YOLO-based architecture with integrated attention mechanisms, termed YOLO-AMC YOLO with Attention Mechanisms for Crack Detection , to enhance automated crack detection performance. Based on YOLOv11, the original C2PSA module is removed, and multiple attention mechanisms, including Global Attention Mechanism GAM , Residual Convolutional Block Attention Module Res-CBAM , and Shuffle Attention SA , are introduced into the multi-scale feature fusion layers of the Neck to strengthen cross-scale feature integration. Experimental results demonstrate that YOLO-AMC consistently outperforms baseline models YOLOv11n and YOLOv8n across multiple evaluation metrics. Among the evaluated attention modules, GAM achieves the best detection performance, obtaining mAP@0.5 = 0.9917 and mAP@0.5:0.95 = 0.9506 on the test dataset, which are higher than those of YOLOv11 0.9833 / 0.9112 and YOLOv8 0.9707 / 0.8921 . Furthermore, while maintaining a computational complexity of 7.6 GFLOPs, the proposed model achieves 110.95 FPS on an NVIDIA RTX 4090 platform and approximately 5 FPS on a Raspberry Pi 5 edge device, demonstrating a favorable trade-off between accuracy and deployment efficiency. The implementation code for this study is available on GitHub at https://github.com/CY-Tsai24/YOLO-AMC.