基于改进Cascade R-CNN的探地雷达管线目标检测

针对人工识别探地雷达管线图像时效率低、误差大和成本高昂等问题, 本文提出了一种基于改进Cascade R-CNN的管线目标智能化检测方法. 首先对探地雷达管线图像数据集进行预处理, 提升数据质量. 然后采用ResNeXt代替ResNet作为主干网络提取目标特征信息, 并添加多尺度特征融合模块FPN使高层特征向低层特征融合, 增强低层特征表达能力. 其次, 使用高斯形式的非极大值抑制方法Soft-NMS得到更加精准的候选框, 使用Smooth_L1作为损失函数, 加速了模型收敛并且降低了训练中发生梯度爆炸的概率. 最后, 对于管线目标特殊的形状特征, 设置合适的锚框长宽比和大小, 提高锚框的生成质量. 实验结果表明, 本文方法解决了复杂特征的地下管线目标智能化检测, 对地下管线目标检测的平均精度达到94.7%, 比Cascade R-CNN方法提高了10.1%.

GPR Pipeline Target Detection Based on Improved Cascade R-CNN

As manual identification of ground-penetrating radar (GPR) pipeline images faces the problems of low efficiency, large errors, and high costs, this study proposes an intelligent pipeline target detection method based on improved Cascade R-CNN. First, the GPR pipeline image data set is preprocessed to improve data quality. ResNeXt is used instead of ResNet as the backbone network to extract target feature information, and a multi-scale feature fusion module FPN is added to fuse high-level features to low-level features to enhance the expressiveness of low-level features. Secondly, the Gaussian non-maximum suppression (NMS) method Soft-NMS is used to obtain more accurate candidate boxes, and Smooth_L1 is taken as the loss function, which accelerates model convergence and reduces the probability of gradient explosion during training. Finally, for the special shape features of the pipeline target, the appropriate aspect ratio and size of the anchor boxes are set to improve the quality of generated anchor boxes. The experimental results demonstrate that the proposed method achieves the intelligent detection of underground pipeline targets with complex features, and the average accuracy of target detection reaches 94.7%, which is 10.1% higher than that of the Cascade R-CNN method.