基于卷积神经网络的细长路面病害检测方法

Elongated pavement distress detection method based on convolutional neural network

针对细长路面病害人工检测耗时长和当前检测方法精度不足的问题,依据病害的弱语义特性和异常几何属性,提出了能够精准定位和分类出病害的二阶段细长路面病害检测方法Epd RCNN。首先,针对细长路面病害的弱语义特性,提出了一种复用低层特征并反复融合不同阶段特征的骨干网络;其次,在训练过程中,使用一种符合病害几何属性分布的锚框机制来生成高质量的正样本供网络训练;然后,在单一高分辨率特征图上预测病害包围框,并针对该特征图使用并行级联空洞卷积模块来提升其多尺度特征表达能力;最后,针对形状各异的候选区域,使用由可变形感兴趣区域池化(RoI Pooling)和空间注意力模块组成的候选区域特征改良模块来提取符合病害几何属性的候选区域特征。实验结果表明,所提方法在光照充足图像上的平均准确率均值(mAP)为0.907,在存在光照问题图像上的mAP为0.891,综合mAP为0.899,表明该方法具有良好的检测性能和对光照的鲁棒性。

Focusing on the problems of the large time consumption of manual detection and the insufficient precision of the current detection methods of elongated pavement distress,a two-stage elongated pavement distress detection method,named Epd RCNN(Elongated pavement distress Region-based Convolutional Neural Network),which could accurately locate and classify the distress was proposed according to the weak semantic characteristics and abnormal geometric properties of the distress.Firstly,for the weak semantic characteristics of elongated pavement distress,a backbone network that reused low-level features and repeatedly fused the features of different stages was proposed.Secondly,in the training process,the high-quality positive samples for network training were generated by the anchor box mechanism conforming to the geometric property distribution of the distress.Then,the distress bounding boxes were predicted on a single high-resolution feature map,and a parallel cascaded dilated convolution module was used to this feature map to improve its multi-scale feature representation ability.Finally,for different shapes of region proposals,the region proposal features conforming to the distress geometric properties were extracted by the proposal feature improvement module composed of deformable Region of Interest Pooling(RoI Pooling)and spatial attention module.Experimental results show that the proposed method has the mean Average Precision(mAP)of 0.907 on images with sufficient illumination,the mAP of 0.891 on images with illumination problems and the comprehensive mAP of 0.899,indicating that the proposed method has good detection performance and robustness to illumination.