Automated Pavement Distress Detection and Deterioration Analysis Using Street View Map

Xu Lei,Chenglong Liu,Li Li,Guiping Wang

doi:10.1109/access.2020.2989028

Xu Lei, Chenglong Liu + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.2989028

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Abstract

Automated pavement distress detection benefits road maintenance and operation by providing the condition and location of various distress rapidly. Existing work generally relies on manual labor or specific algorithms trained by dedicated datasets, which hinders the efficiency and applicable scenarios of methods. Street view map provides interactive panoramas of a large scale of urban roadway network, and is updated in a recurrent manner by the provider. This paper proposed a deep learning method based on a pre-trained neural network architecture to identify and locate different distress in real-time. About 20,000 street view images were collected and labeled as the training dataset using the Baidu e-map. Eight types of distress are notated using Yolov3 deep learning architecture. The scale-invariant feature transform (SIFT) descriptors combined with GPS and bounding boxes were applied to judge the deterioration of the distress. A decision tree was designed to evaluate the change of the distress over some time. A typical road in Shanghai was selected to verify the effectiveness of the proposed model. The images of the road from 2015 to 2017 were collected from the street view map. The results showed that the mean average precision of the proposed algorithm is 88.37%, demonstrating the vast potential of applying this method to detect pavement distress. 43 distress were newly generated, and 49 previous distress were patched in the two years. The proposed method can assist the authorities to schedule the maintenance activities more effectively.

Highlights

Pavement distress detection plays a vital role in road maintenance and management
CONVOLUTIONAL NEURAL NETWORK FOR DISTRESS DETECTION A deep learning-based object detection model YOLOv3 (You Only Look Once version 3) was applied to detect and locate various pavement distress at the same time
One of the advantages of street view maps is that it can output the external parameters of the vehicle-mounted camera when retrieving the image from a fixed GPS

Summary

INTRODUCTION

Pavement distress detection plays a vital role in road maintenance and management. It provides essential information for various pavement performance evaluation, including crack, pothole, corrugation, reveling, patching, etc. This paper combines the advantages of deep convolutional neural network and image texture features to propose a hybrid model for distress detection and matching based on the street view images. CONVOLUTIONAL NEURAL NETWORK FOR DISTRESS DETECTION A deep learning-based object detection model YOLOv3 (You Only Look Once version 3) was applied to detect and locate various pavement distress at the same time. The main advantage of Yolo is that it is a one-stage model, which eliminates the process of traditional region proposal algorithm and unifies the object detection and classification into a regression problem This one-stage framework takes the original image as input and directly outputs the prediction results, achieving an end-to-end training using multi-scale feature extraction. A feature map with eight times down-sampled relative to the input image is obtained It has the smallest receptive field and thereby applied for recognizing the small-sized objects.

IMAGE PERSPECTIVE TRANSFORMATION

DETERIORATION EVALUATION USING BOUNDING BOX AND SIFT FEATURE POINTS

RESULTS OF DETERIORATION ANALYSIS A typical urban road

Findings

CONCLUSION