【摘要】 针对现有深度学习方法在进行大坝裂缝检测时出现模型过拟合、计算效率低下等问题，文中提出了一种基于多源迁移学习的大坝裂缝检测方法，旨在提高算法准确率的同时，减少模型计算量，加快检测速度。所提方法首先将MobileNet网络和SSD目标检测算法相结合，形成MobileNet-SSD网络，有效减少了模型参数量并减少了计算复杂度；然后利用道路裂缝、墙壁裂缝和桥梁裂缝等多源数据进行训练，并应用迁移学习的思想，将学习到的知识分别迁移到大坝裂缝的检测模型中，以提升模型检测的精确度；最后提出了一种多模型融合方法，将通过迁移学习得到的多个检测结果进行融合，进一步提升了检测结果的重合度。更多还原

【Abstract】 The existing deep models will encounter overfitting and low computational efficiency when they are directly used for dam crack detection.This paper proposes a new dam crack detection algorithm based on multi-source transfer learning, which aims to improve the accuracy, reduce the model calculation and speed up the detection speed.Firstly, this method combines MobileNet with SSD object detection algorithm to construct a MobileNet-SSD network, which effectively reduces model parameters and computational complexity.Then, the proposed deep network is trained by using multi-source data sets such as road cracks, wall cracks and bridge cracks.Based on the transfer learning idea, the learned knowledge is transferred to the target domain model of dam crack to further improve the detection accuracy.Finally, a multi-model fusion method is proposed to integrate the detection results of different models obtained through transfer learning, which can effectively enhance the location of output boxes.更多还原