Dong Xuwei, Liu Yang, Dai Jinpeng
Key Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China.
National and Provincial Joint Engineering Laboratory of Road & Bridge Disaster Prevention and Control, Lanzhou Jiaotong University, Lanzhou 730070, China.
Sensors (Basel). 2024 Aug 14;24(16):5252. doi: 10.3390/s24165252.
Concrete surface crack detection is a critical research area for ensuring the safety of infrastructure, such as bridges, tunnels and nuclear power plants, and facilitating timely structural damage repair. Addressing issues in existing methods, such as high cost, lengthy processing times, low efficiency, poor effectiveness and difficulty in application on mobile terminals, this paper proposes an improved lightweight concrete surface crack detection algorithm, YOLOv8-Crack Detection (YOLOv8-CD), based on an improved YOLOv8. The algorithm integrates the strengths of visual attention networks (VANs) and Large Convolutional Attention (LCA) modules, introducing a Large Separable Kernel Attention (LSKA) module for extracting concrete surface crack and local feature information, adapted for features such as fracture susceptibility, large spans and slender shapes, thereby effectively emphasizing crack shapes. The Ghost module in the YOLOv8 backbone efficiently extracts essential information from original features at a minimal cost, enhancing feature extraction capability. Moreover, replacing the original convolution structure with GSConv in the neck network and employing the VoV-GSCSP module adapted for the YOLOv8 framework reduces floating-point operations during feature channel fusion, thereby lowering computational complexity whilst maintaining model accuracy. Experimental results on the RDD2022 and Wall Crack datasets demonstrate the improved algorithm increases in mAP50 by 15.2% and 12.3%, respectively, and in mAP50-95 by 22.7% and 17.2%, respectively, whilst achieving a reduced model computational load of only 7.9 × 10, a decrease of 3.6%. The algorithm achieves a detection speed of 88 FPS, enabling real-time and accurate detection of concrete surface crack targets. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of the proposed approach.
混凝土表面裂缝检测是确保桥梁、隧道和核电站等基础设施安全以及促进及时进行结构损伤修复的关键研究领域。针对现有方法存在的高成本、处理时间长、效率低、效果差以及在移动终端上应用困难等问题，本文基于改进的YOLOv8提出了一种改进的轻量级混凝土表面裂缝检测算法，即YOLOv8 - 裂缝检测（YOLOv8 - CD）。该算法融合了视觉注意力网络（VAN）和大卷积注意力（LCA）模块的优势，引入了大分离核注意力（LSKA）模块来提取混凝土表面裂缝和局部特征信息，适用于裂缝敏感性、大跨度和细长形状等特征，从而有效突出裂缝形状。YOLOv8主干中的Ghost模块以最小成本从原始特征中高效提取关键信息，增强了特征提取能力。此外，在颈部网络中用GSConv替换原始卷积结构，并采用适用于YOLOv8框架的VoV - GSCSP模块，减少了特征通道融合过程中的浮点运算，从而在保持模型精度的同时降低了计算复杂度。在RDD2022和墙面裂缝数据集上的实验结果表明，改进后的算法mAP50分别提高了15.2%和12.3%，mAP50 - 95分别提高了22.7%和17.2%，同时模型计算负载仅为7.9×10，降低了3.6%。该算法实现了88帧每秒的检测速度，能够实时、准确地检测混凝土表面裂缝目标。与其他主流目标检测算法的比较验证了所提方法的有效性和优越性。
