Corpus ID: 2625582

Development of Crack Detection System with Unmanned Aerial Vehicles and Digital Image Processing

  title={Development of Crack Detection System with Unmanned Aerial Vehicles and Digital Image Processing},
  author={Jong-woo Kim and Sungbae Kim and Jeong-Cheon Park and Jin-Won Nam},
Conventional crack detecting inspections of structures have been mainly based on visual investigation methods. Huge and tall structures such as cable bridges, highrising towers, dams and industrial power plants are known to have its inaccessible area and limitation in field inspection due to its geometry. In some cases, inspection of critical structural members is not possible due to its spatial constraints. With rapid technical development of unmanned aerial vehicle (UAV), the limitation of… Expand

Figures and Tables from this paper

Localization of an unmanned aerial vehicle for crack detection in railway tracks
A real-time implementation of the proposed method can significantly reduce physical labor involved in crack detection and also reduces the risk of accidents. Expand
Diagnosis of crack damage on structures based on image processing techniques and R-CNN using unmanned aerial vehicle (UAV)
Techniques to identify and quantify the damage to bridges based on images obtained by the unmanned aerial vehicle (UAV) based on Deep-learning and algorithms of detection and quantification using improved Image Processing Techniques (IPTs). Expand
UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks
The proposed framework for detecting cracks along with their locations is proven as an effective way to detect cracks and to represent the cracks’ locations. Expand
Robust image-based crack detection in concrete structure using multi-scale enhancement and visual features
A new crack detection framework based on multi-scale enhancement and visual features is developed, in which the adaptive threshold algorithm is used to obtain the binary image and the combination of morphological processing andVisual features are adopted to purify the cracks. Expand
Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network
This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection, and instantly transformed into a global bridge damage map. Expand
Accuracy Assessment of Detecting Cracks on Concrete Wall at Different Distances using Unmanned Autonomous Vehicle (UAV) Images
Analysis of the accuracy of the UAV image in detecting cracks at different distance measurements qualitatively and quantitatively allows for a better understanding on how distance affects the detection of the crack which may be useful for building inspections or in the engineering sector. Expand
Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach
In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansionExpand
Deep learning-based concrete crack detection using hybrid images
The proposed technique is able to achieve automated crack identification by modifying a well-trained convolutional neural network using a set of crack images as a training image set, while retaining the advantages of hybrid images. Expand
Concrete Cracks Detection Based on Deep Learning Image Classification
A machine learning-based model to detect cracks on concrete surfaces that relies on a deep learning convolutional neural network (CNN) image classification algorithm to increase the level of automation on concrete infrastructure inspection when combined to unmanned aerial vehicles (UAV). Expand
Diversion Tunnel Defects Inspection and Identification Using an Automated Robotic System
A crawler teleoperation robot system (CTRS) for diversion tunnel structural inspection and a high-precision defects classification method suitable for hydraulic diversion tunnel defects are proposed. Expand


Morphological segmentation and classification of underground pipe images
The experimental results demonstrate that the proposed algorithm can precisely segment and classify pipe cracks, holes, laterals, joints and collapse surface from underground pipe images. Expand
A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures
Automatic health monitoring and maintenance of civil infrastructure systems is a challenging area of research. Nondestructive evaluation techniques, such as digital image processing, are innovativeExpand
A vision system for automated crack detection in welds
The authors describe the development of a vision-based system for automated inspection of surface cracks in materials. The magnetic particle inspection method is used to reveal cracks inExpand
Image registration methods: a survey
A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas. Expand
A Combined Corner and Edge Detector
The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide aExpand
A survey of image registration techniques
This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied, and establishing a framework for understanding the merits and relationships between the wide variety of existing techniques. Expand
Distinctive Image Features from Scale-Invariant Keypoints
The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used toExpand
Digital Image Processing
Morphological segmentation and classification
  • 2006
Comparison of some morphological segmentation algorithms based on contrast enhancement . application to automatic defect detection
  • Proceedings of the EUSIPCO90Fifth European Signal Processing Conference
  • 1990