Abstract
With the rapid development of computer vision and machine learning, automated methods for pothole detection and recognition based on image and video data have received significant attention. It is of great significance for social development to conduct an in-depth analysis of road images through feature extraction, thereby achieving automatic identification of the pothole condition in new images. Consequently, this is the main issue addressed in this study. Based on preprocessing techniques such as standardization, normalization, and data augmentation applied to the collected raw dataset, we continuously improved the network model based on experimental results. Ultimately, we constructed a deep learning feature extraction network ResNet50-EfficientNet-RegNet model based on transfer learning. This model exhibits high classification accuracy and computational efficiency. In terms of model evaluation, this study employed a comparative evaluation approach by comparing the performance of the proposed transfer learning model with other models, including Random Forest, MLP, SVM, and LightGBM. The comparison analysis was conducted based on metrics such as Accuracy, Recall, Precision, F1-score, and FPS, to assess the classification performance of the transfer learning model proposed in this paper. The results demonstrate that our model exhibits high performance in terms of recognition speed and accuracy, surpassing the performance of other models. Through careful parameter selection and model optimization, our transfer learning model achieved a classification accuracy of 97.78% (88/90) on the initial set of 90 test samples and 98.89% (890/900) on the expanded test set.
Abstract (translated)
随着计算机视觉和机器学习的迅速发展，基于图像和视频数据自动检测和识别路面坑洞的方法受到了广泛关注。通过特征提取对道路图像进行深入分析，从而实现新图像中路面状况的自动化识别，这对社会发展具有重要意义，这也是本研究的主要关注点。 在收集到的原始数据集基础上，我们应用了诸如标准化、归一化和数据增强等预处理技术，并根据实验结果不断改进网络模型。最终，基于迁移学习构建了一个深度学习特征提取网络ResNet50-EfficientNet-RegNet模型。该模型表现出高分类准确率和计算效率。 在模型评估方面，本研究采用了一种比较评价方法，将提出的迁移学习模型与随机森林、多层感知器(MLP)、支持向量机(SVM)以及LightGBM等其他模型进行性能对比分析，并基于准确性(Accuracy)、召回率(Recall)、精确度(Precision)、F1值(F1-score)和FPS(Frame Per Second，每秒帧数)等指标来评估本文提出的迁移学习模型的分类性能。实验结果表明，我们的模型在识别速度和准确率方面表现出色，优于其他模型的表现。 通过精心选择参数并优化模型，我们的迁移学习模型在最初的90个测试样本中达到了97.78%（88/90）的分类准确率，并且在扩展后的测试集(900个样本)上实现了高达98.89% (890/900) 的分类准确度。
URL
https://arxiv.org/abs/2509.06750
