摘要
深度学习技术现已应用于多工程领域。为实现钢筋混凝土结构裂缝的准确高效检测,在分析传统YOLOv8l算法优劣的基础上,提出了一种改进的裂缝检测网络模型YOLOv8-CD。首先,在普通YOLOv8l模型中引入RepGhost模块,使模型计算量和参数均有所减少;其次,引入卷积注意力机制CBAM使模型能够更加聚焦于裂缝区域,提升检测的精准度和鲁棒性;最后,融入广义特征金字塔网络GFPN与优化损失函数,使目标的特征显示得更全面。实验结果表明:改进后的YOLOv8-CD模型表现出更高的准确性,平均精度达到了96.4%,相比基准模型提升了约23.4个百分点;改进模型在检测精度和实时性方面取得了良好的平衡。
Deep learning technology has been widely applied across various engineering fields.To achieve accurate and efficient detection of concrete cracks,an enhanced reinforced concrete crack detection network model—named YOLOv8-Crack Damage(YOLOv8-CD)—was proposed based on an analysis of the strengths and limitations of the conventional YOLOv8l algorithm.First,the RepGhost module was introduced into the original YOLOv8l model,reducing both computational load and the number of parameters.Second,a convolutional attention mechanism(CBAM)was incorporated,enabling the model to focus more effectively on crack regions and thereby improving the accuracy and robustness of concrete crack detection.Finally,the Global Feature Pyramid Network(GFPN)and an optimized loss function were integrated to enhance the comprehensiveness of target feature representation.Engineering application results demonstrated that the improved YOLOv8-CD model achieved higher accuracy,with a mean average precision of 96.4%,an increase of approximately 23.4 percentage points over the baseline model.Additionally,the enhanced model effectively gains a balance between detection accuracy and real-time performance.
作者
任青阳
王彦丁
施俭
肖宋强
REN Qingyang;WANG Yanding;SHI Jian;XIAO Songqiang(State Key Laboratory of Mountain Bridge and Tunnel Engineering,Chongqing Jiaotong University,Chongqing 400074,China;School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《人民长江》
北大核心
2025年第12期237-245,共9页
Yangtze River
基金
国家重点研发计划项目(2023YFC3008300,2023YFC3008304-4)
国家自然科学基金项目(U20A20314)。
