基于改进单点多盒检测器的大坝缺陷目标检测方法

Dam defect object detection method based on improved single shot multibox detector

为提升大坝安全运维的效率,大坝缺陷目标检测模型有助于辅助巡检人员进行缺陷检测。大坝缺陷几何形状多变,而采用传统卷积方式进行特征提取的单点多盒检测器(SSD)模型无法适应缺陷的几何变换。针对上述问题,提出可变形卷积单步多框检测器(DFSSD)模型。首先将原始SSD的主干网络VGG16中的标准卷积替换为可变形卷积,用于处理缺陷的几何变换,并且通过学习卷积偏移量来提升模型的空间信息建模能力;其次针对不同特征的尺寸,改进先验框比例,从而提高模型对条形特征的检测精度与模型的泛化能力;最后为解决训练集正负样本不均衡的问题,采用改进的非极大值抑制(NMS)算法来优化学习效果。实验结果表明:DFSSD模型较基准模型SSD在大坝缺陷图像上的平均检测精度提升了5.98%。相较于基于区域的更快卷积神经网络(Faster R-CNN)和SSD模型,DFSSD模型在大坝缺陷目标检测精度提升上有较好的效果。

In order to improve the efficiency of dam safety operation and maintenance,the dam defect object detection models can help to assist inspectors in defect detection.There is variability of the geometric shapes of dam defects,and the Single Shot MultiBox Detector(SSD)model using traditional convolution methods for feature extraction cannot adapt to the geometric transformation of defects.Focusing on the above problem,a DeFormable convolution Single Shot multi-box Detector(DFSSD)was proposed.Firstly,in the backbone network of the original SSD:Visual Geometry Group(VGG16),the standard convolution was replaced by the deformable convolution,which was used to deal with the geometric transformation of defects,and the model’s spatial information modeling ability was increased by learning the convolution offset.Secondly,according to the sizes of different features,the ratio of the prior bounding box was improved to prompt the detection accuracy of the model to the bar feature and the model’s generalization ability.Finally,in order to solve the problem of unbalanced positive and negative samples in the training set,an improved Non-Maximum Suppression(NMS)algorithm was adopted to optimize the learning effect.Experimental results show that the average detection accuracy of DFSSD is improved by 5.98% compared to the benchmark model SSD on dam defect images.By comparing with Faster Region-based Convolutional Neural Network(Faster R-CNN)and SSD models,it can be seen that DFSSD model has a better effect in improving the detection accuracy of dam defect objects.