Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks
Cracks cause deterioration of road performance and functional or structural failure if not managed in a timely manner. This paper proposes an automated crack detection method using a car black box camera to address this problem. The proposed method uses a deep learning model [i.e., convolutional neural network (CNN)] composed of segmentation and classification modules. The segmentation process is performed to extract only the road surface in order to remove elements that interfere with crack detection in the black box image. Then, cracks are detected through analysis of patch units within the extracted road surface. The proposed CNN architecture classifies the elements of the road surface into three categories (i.e., crack, road marking, and intact area) with 90.45% accuracy. The results of the proposed CNN architecture are better than those of previous studies.
Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks
Cracks cause deterioration of road performance and functional or structural failure if not managed in a timely manner. This paper proposes an automated crack detection method using a car black box camera to address this problem. The proposed method uses a deep learning model [i.e., convolutional neural network (CNN)] composed of segmentation and classification modules. The segmentation process is performed to extract only the road surface in order to remove elements that interfere with crack detection in the black box image. Then, cracks are detected through analysis of patch units within the extracted road surface. The proposed CNN architecture classifies the elements of the road surface into three categories (i.e., crack, road marking, and intact area) with 90.45% accuracy. The results of the proposed CNN architecture are better than those of previous studies.
Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks
Park, Somin (Autor:in) / Bang, Seongdeok (Autor:in) / Kim, Hongjo (Autor:in) / Kim, Hyoungkwan (Autor:in)
28.02.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
| Online Contents | 2017
Interpretability Analysis of Convolutional Neural Networks for Crack Detection
| DOAJ | 2023
Fast Crack Detection Using Convolutional Neural Network
| TIBKAT | 2021