Assessing severity of road cracks using deep learning-based segmentation and detection

Abstract

For the successful operation of pavement management system, it is necessary to automate the detection, classification, and severity assessment of road cracks, which are bottlenecks in the entire process. Although good results for the detection and classification of road cracks have been published in many related studies, the number of crack types detected is still insufficient for actual field use, and studies on crack severity assessment are difficult to find. In this study, the number of crack types are expanded to five types (alligator crack, longitudinal crack, transverse crack, pothole, and patching) to meet the needs of fieldwork, and the assessment of crack severity is also included in the proposed model. In this system constructed using SqueezeNet, U-Net, and Mobilenet-SSD models together, an accuracy of 91.2% has been achieved for both crack type and severity assessment. The authors have performed segmentation of the input images using separately trained U-Nets for linear cracking and area cracking to improve object detection performance and automate crack severity assessment. With reference to the system presented in this study, it is expected that an automated pavement management system that better reflects each country’s requirements for various crack types and severity standards is possible.

Crack Management System Using the Embedded Device for Drone Mounting

“…교량은 시간이 지남에 따라서 내부와 외부에 물리적인 힘이 작용하면서 균열, 파손, 변형, 침식이 발생한다. 산업의 발달에 따라서 교량을 이용하는 차량이 증가하고 있으며, 점차 차량의 대형화에 따라서 교량의 노후화가 급격히 진행되고 있다 [1] [2]. 특히 외부 재료로 콘크리트를 사용하여 만들어진 철근콘크리트의 경우 외부 충격에 의해서 콘크리트에 쉽게 균열이 가거나 부서지기 쉽기 때문에 주기적으로 교량 안전 점검을 통해서 위험요소를 발견하고 유지보수를 해야한다.…”

“…교량 안전 점검을 위해서 이루어지는 정기점검은 육안검사를 통해 교량의 외관상태를 중심으로 점검하는 방식으로 정밀점검에 비해서 비용과 시간이 단축되기 때문에 주기적으로 진행할 수 있는 안전점검 방식이다 [3] [4]. 교량을 안전검검하기 위해서는 특수차량 및 장비를 이용하여 숙련된 전문인력이 검사를 진행하지만 안정장치의 문제와 높은 위치에 있는 부분은 특수 크레인과 바지선이 필요하기 때문에 점검이 어려운 경우도 있다 [1][2][3]. 이러한 문제점을 해결하기 위해서 머신러닝 알고리즘을 활용하여 균열 유무를 검사하는 연구가 활발하게 진행되고 있다 [3][4][5][6] Crack Management Server { "device": "device number", "latitude": "latitude value", "longitude": "longitude value", "altitude": "altitude value", "distance": "distance value", "status": "start or stop", "model": "model name", "frame": "frame value", "resolution": "resolution value", "timestamp": "current timestamp", }…”

Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model

“…Shang et al presented a multi-fusion U-Net network to automatically detect the pixel-level pavement crack with superior performance compared to seven other state-of-the-art models [15]. Recently, Ha et al develop a novel system integrating SqueezeNet, U-Net and mobilenet-SSD models together for detection, classification, and severity assessment of five types road cracks [16]. This combined system is capable of crack severity assessment and crack type classification with a reported accuracy of 91.2%.…”

“…This combined system is capable of crack severity assessment and crack type classification with a reported accuracy of 91.2%. The recent success of deep learning methods for crack detection applications demonstrates the feasibility of fine-tuning CNNs within an ample parameter space [2][3][4][5][11][12][13][14][15][16]. For instance, the CNN models, such as ConvNet, U-Net, and YOLO adopted in [11,14,15,16], have more than 60 million parameters.…”

“…The recent success of deep learning methods for crack detection applications demonstrates the feasibility of fine-tuning CNNs within an ample parameter space [2][3][4][5][11][12][13][14][15][16]. For instance, the CNN models, such as ConvNet, U-Net, and YOLO adopted in [11,14,15,16], have more than 60 million parameters. The FCN2s network for pixel-level crack identification has 144 million parameters [4].…”