计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 616-619.doi: 10.11896/jsjkx.201200059
彭磊, 张辉
PENG Lei, ZHANG Hui
摘要: 道路是现代交通运输最主要的途径之一,道路缺陷对于道路安全有着巨大威胁。因此准确检测道路缺陷对道路养护修缮具有重要意义。道路缺陷具有低连续性和低对比度的特点,现阶段多采用人工检测方法,检测效率低,人力成本高,且检测人员的安全可能会遭受威胁。随着深度学习的发展,神经网络方法被广泛应用于工程实践。U-net是具有编码器-解码器结构的端到端深度学习模型,对微小对象检测能力强,适用于道路裂缝缺陷检测。利用U-net深度学习网络对道路缺陷进行检测,能提高检测效率,无需人工干预,保证检测人员安全,降低检测的人工成本。实验结果表明,U-net网络在数据集Crack500上的效果优于FCN,Segnet等语义分割网络,在保持较高精度的情况下实现了道路缺陷检测。在此基础上对U-net网络层数进行超参数优化,确定该数据集上的最优U-net网络结构。
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