道路病害的诊断是道路预防保养的一个关键步骤,为此本文提出了一种基于RDNet (Road Detection Network)道路病害检测算法。该算法从不同角度提高了特征的提取和表达能力,其中的改进包括跨阶段多分支卷积、残差并行空洞卷积以及自适应尺...道路病害的诊断是道路预防保养的一个关键步骤,为此本文提出了一种基于RDNet (Road Detection Network)道路病害检测算法。该算法从不同角度提高了特征的提取和表达能力,其中的改进包括跨阶段多分支卷积、残差并行空洞卷积以及自适应尺度空间注意力模块等。通过在自建的道路病害数据集上进行端到端地训练,提高了算法的检测精度和泛化能力。实验结果表明,对比YOLOv5s,本文所提出的RDNet算法的平均精度均值mAP提高了1.3%,同时对于困难样本也有较好的检测结果,能够有效地应用于实际道路的维护工作中,从而提升道路病害检测的效率和准确性。The diagnosis of road diseases is a key step in road preventive maintenance, so this paper proposes a road disease detection algorithm based on RDNet (Road Detection Network). The algorithm improves the ability of feature extraction and expression from different perspectives, including crossstage partial multi-branch convolution, residual parallel dilated convolution, and adaptive scale spatial attention module. End-to-end training on the self-built road disease dataset improves the detection accuracy and generalization ability of the algorithm. Experimental results show that compared with YOLOv5s, the average precision of the RDNet algorithm proposed in this paper is increased by 1.3%, and the average precision mAP of the proposed RDNet algorithm is improved by 1.3%, and it also has good detection results for difficult samples, which can be effectively applied to the maintenance of actual roads, so as to improve the efficiency and accuracy of road disease detection.展开更多
文摘道路病害的诊断是道路预防保养的一个关键步骤,为此本文提出了一种基于RDNet (Road Detection Network)道路病害检测算法。该算法从不同角度提高了特征的提取和表达能力,其中的改进包括跨阶段多分支卷积、残差并行空洞卷积以及自适应尺度空间注意力模块等。通过在自建的道路病害数据集上进行端到端地训练,提高了算法的检测精度和泛化能力。实验结果表明,对比YOLOv5s,本文所提出的RDNet算法的平均精度均值mAP提高了1.3%,同时对于困难样本也有较好的检测结果,能够有效地应用于实际道路的维护工作中,从而提升道路病害检测的效率和准确性。The diagnosis of road diseases is a key step in road preventive maintenance, so this paper proposes a road disease detection algorithm based on RDNet (Road Detection Network). The algorithm improves the ability of feature extraction and expression from different perspectives, including crossstage partial multi-branch convolution, residual parallel dilated convolution, and adaptive scale spatial attention module. End-to-end training on the self-built road disease dataset improves the detection accuracy and generalization ability of the algorithm. Experimental results show that compared with YOLOv5s, the average precision of the RDNet algorithm proposed in this paper is increased by 1.3%, and the average precision mAP of the proposed RDNet algorithm is improved by 1.3%, and it also has good detection results for difficult samples, which can be effectively applied to the maintenance of actual roads, so as to improve the efficiency and accuracy of road disease detection.
