计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 231-236.doi: 10.11896/jsjkx.211200290
瞿中, 王彩云
QU Zhong, WANG Caiyun
摘要： 混凝土路面上的裂缝会影响结构的安全性、适用性和耐久性,裂缝检测是一个充满挑战的研究热点。文中提出了由改进的全卷积网络和深监督网络组成的裂缝检测模型,以改进的VGG-16作为主干网络,首先将低层卷积特征聚合,通过空间注意力机制再次融合到主干网络;其次,将中高层卷积特征通过轻量级空洞卷积融合模块进行多尺度融合得到具有清晰边缘且分辨率较高的特征图像,所有的侧边特征图像相加产生最终的预测图像;最后,深监督网络为每个阶段的检测结果提供直接监督。该网络选择焦点损失函数作为评价函数,经过训练的网络模型能够在光照不均、背景复杂等各种条件下从输入的原始图像中高效地识别出裂缝位置。为验证所提方法的有效性和鲁棒性,在DeepCrack,CFD,Crack500这3个数据集上与6种方法进行了比较,所提算法表现出卓越的性能,F-score值达到了87.12%。
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