改进Mask R-CNN公路病害检测算法

针对公路路面病害与背景像素对比度低导致检测困难的问题,本文提出了改进Mask R-CNN公路病害检测算法(FAC-Mask R-CNN)。首先在ResNet101基础上增加强位置信息浅层特征表达,并融合相邻特征图作为主干网络最终特征输出,同时引入CBAM模块,以减弱目标与背景像素间低对比度的影响;然后采用深度可分离卷积和空洞卷积分别代替主干网络及有效特征层输出过程应用的普通卷积,提升模型计算效率及掩码预测精度。FAC-Mask R-CNN在公路路面病害数据集(RDD)上平均精确率为89.86%,召回率为88.54%,调和均值为90%,相较于Mask R-CNN算法平均精确率提升3.09%。结果表明,FAC-Mask R-CNN能有效完成公路路面病害精细化检测与分割任务。

Improved Mask R-CNN highway disease detection algorithm

An improved Mask R-CNN highway disease detection algorithm (FAC-Mask R-CNN) is proposed to solve the problem of detection difficulty caused by road surface disease and low background pixel contrast. Based on ResNet101, a shallow feature expression with strong position information is added, and the adjacent feature maps are first fused as the final feature output of the backbone network. At the same time, the CBAM module is introduced to reduce the effect of low contrast between target and background pixels. Deep separable convolution and void convolution are used to replace the common convolution applied in backbone network and effective feature layer output process, respectively, which can improve the computational efficiency and mask prediction accuracy of the model. The average accuracy rate of FAC-Mask R-CNN on RDD(road disease datasets) is 89.86%, the recall rate is 88.54%, and the harmonic mean is 90%, which is 3.09% higher than that of Mask R-CNN algorithm. The results show that FAC-Mask R-CNN can effectively complete the task of fine detection and segmentation of road surface diseases.