Cui Peng, Bidzikrillah Nurjihan Ala, Xu Jiancong, Qin Yazhou
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Jiangsu Water Source Company Ltd. of the Eastern Route of the South-to-North Water Diversion Project, Nanjing 210018, China.
Sensors (Basel). 2024 Sep 23;24(18):6130. doi: 10.3390/s24186130.
Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network's decisions and guide measures to improve its performance. The results demonstrate that the model's classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps.
路面质量对于驾驶员的舒适度和安全性至关重要，因此实时监测路面状况并检测缺陷至关重要。然而，缺陷的多样性和环境条件的复杂性使得开发一种有效且强大的分类和检测算法具有挑战性。在本研究中，我们采用半监督学习方法来训练ResNet-18进行图像特征检索，然后对路面缺陷进行分类和检测。从图像块中获取的特征嵌入向量被检索、连接并随机采样，以基于唯一的一类训练路面图像数据集对多元正态分布进行建模。校准路面图像数据集用于根据接收器操作特征曲线确定缺陷分数阈值，采用马氏距离作为评估正常路面图像和缺陷路面图像之间差异的指标。最后，将测试数据集的缺陷分数图导出的热图叠加在原始路面图像上，以深入了解网络的决策并指导提高其性能的措施。结果表明，基于热图分析，使用扩展和增强的路面图像数据，模型的分类准确率从0.868提高到了0.887。
