计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 220900155-5.doi: 10.11896/jsjkx.220900155
张恩华, 王卫杰, 段楠, 康楠
ZHANG Enhua, WANG Weijie, DUAN Nan, KANG Nan
摘要： 为探究湿润环境对沥青路面裂缝自动检测效果的影响,文章通过YOLOv5深度学习目标检测算法,建立了沥青路面裂缝检测模型;并在此基础上,设置了湿润与干燥环境下的裂缝检测对比实验,对比了在两种环境下沥青路面裂缝检测结果的准确度与置信度。研究结果表明,湿润环境扩大了沥青路面裂缝在深度学习网络中的识别特征,提高了裂缝检测的效果。干燥路面裂缝检测的准确度为80.70%,湿润路面裂缝检测的准确度为89.47%,湿润环境下的沥青路面裂缝检测模型准确率提升了8.77%。同时,统计同一裂缝两种环境下检测的置信度发现,置信度平均值在干燥环境下为0.72,在湿润环境下为0.78,且湿润与否与裂缝检测的置信度存在显著正相关关系。研究成果为沥青路面裂缝自动检测效果的提升提供了新的思路,为路面养护管理的决策提供了有效工具。
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