高速公路路面病态识别模型

1、定义目标检测网络，采用 Faster R-CNN结构，通过预训练的卷积神经网络进行特征提取，通过卷积层和池化层，进行图像标注区域的处理；在输入层图像定义与高速公路路面病态图像标注样本集的图像匹配的图像尺寸和通道数；通过迭代计算，在输出层输出标签和边界框坐标。
2、高速公路路面病态图像标注样本集作为样本，70%为训练集，30%为验证集；
3、使用高速公路路面病态图像标注样本集进行模型训练，通过标准原始图像特征与标注信息之间的映射关系，从而实现对图像分类、病害检测和和标注。

1. An object detection network is defined based on the Faster R-CNN architecture. A pre-trained convolutional neural network is employed for feature extraction, with convolutional layers and pooling layers used to process annotated image regions. The input layer is configured with image size and number of channels matching those of samples in the highway pavement distress image annotation dataset. Through iterative computation, the output layer outputs labels and bounding box coordinates.
2. The highway pavement distress image annotation dataset is split into a training set (70%) and a validation set (30%).
3. The model is trained using the highway pavement distress image annotation dataset, leveraging the mapping relationship between features of standard raw images and annotation information to achieve image classification, disease detection and annotation.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于高速公路路面病害识别的机器学习模型，数据规模为8M，通过航拍图片自动识别和标注病害位置、类型和参数。它基于Faster R-CNN结构进行训练，适用于交通运输行业的道路检测场景，旨在提升路面病害检测的效率和准确性。