Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network

Abstract

Pavement surveying and distress mapping is completed by roadway authorities to quantify the topical and structural damage levels for strategic preventative or rehabilitative action. The failure to time the preventative or rehabilitative action and control distress propagation can lead to severe structural and financial loss of the asset requiring complete reconstruction. Continuous and computer-aided surveying measures not only can eliminate human error when analyzing, identifying, defining, and mapping pavement surface distresses, but also can provide a database of road damage patterns and their locations. The database can be used for timely road repairs to gain the maximum durability of the asphalt and the minimum cost of maintenance. This paper introduces an autonomous surveying scheme to collect, analyze, and map the image-based distress data in real time. A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. Typically, CNN-based schemes require a relatively large processing power to detect desired objects in images in real time. However, the portability objective of this work requires to utilize low-weight processing units. To that end, the CNN training was optimized by the Bayesian optimization algorithm (BOA) to achieve the maximum accuracy and minimum processing time with minimum neural network layers. First, a database consisting of a diverse population of crack distress types such as longitudinal, transverse, and alligator cracks, photographed at multiple angles, was prepared. Then, the database was used to train a CNN whose hyperparameters were optimized using BOA. Finally, a heuristic algorithm is introduced to process the CNN’s output and produce the crack map. The performance of the classifier and mapping algorithm is examined against still images and videos captured by a drone from cracked pavement. In both instances, the proposed CNN was able to classify the cracks with 97% accuracy. The mapping algorithm is able to map a diverse population of surface cracks patterns in real time at the speed of 11.1 km per hour.

Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

“…In this comparison analysis, we try to provide some fair comparison with proposed system and existing scheme based on some key differences. From above table, Crack U-net [ 50 ] and Deep encoder-decoder [ 25 ] are based on pixel-wise crack detection architecture and trained with 3000 and 600 defect image respectively. Here, Crack U-net obtained detection accuracy of , which follows the FCN style CNN layers.…”

“…However, both models need a larger computing resource and less suitable for in-situ inspection. In [ 25 , 48 ] implementations are developed for real-time remote defect inspection where multi-layer CNN and drone are used by Naddaf-Sh et al and model process 5 frames per second and achieve detection accuracy [ 25 ]. other-hand FCN - Gaussian-conditional random field combination was used by Zheng Tong et al.…”

“…Here, DCNN was used to determine the defect in the image, bilateral filtering for smoothed the crack region, and adaptive threshold method from extract the cracks from the road surface. The real-time road crack mapping system was proposed in [ 25 ] where the crack detection network was trained with longitudinal, transverse, and alligator type defects images and optimized by the Bayesian optimization algorithm. The author reported that the crack detection network classifies the road defects with accuracy.…”

Image Processing-Based Spall Object Detection Using Gabor Filter, Texture Analysis, and Adaptive Moment Estimation (Adam) Optimized Logistic Regression Models

“…As can be seen from the literature, a large number of previous studies have been dedicated to crack detection for concrete structures [10][11][12][13][14][15][16][17][18][19][20]. Only recently, there is an increasing focus on detecting other forms of damage including concrete spalling [21][22][23][24].…”

Application of neural network method for road crack detection

“…TELKOMNIKA Telecommun Comput El Control  Application of neural network method for road crack detection (Yuslena Sari) 1963 Futhermore, at a lower cost, there were several studies using computer vision approach for solving this problem in 11 years [12][13][14][15][16][17]. In computer vision, it is first necessary to extract features from the dataset to obtain quantitative data from image data, then the next process is to detect road pavement cracks.…”