Automatic recognition of surface cracks in bridges based on 2D-APES and mobile machine vision

Abstract Compared with the artificial crack detection method, the bridge crack recognition method based on computer vision has the advantages of high efficiency, easy operation and low cost. However, under the condition of moving (UAV) shooting, the crack images collected often have quality defects such as low definition, complex background, severe interference by light and noise. Especially when faced with small cracks in early development, some traditional crack detection algorithms with high requirements on crack images cannot be well adapted. In this paper, an automatic recognition technology for surface cracks of bridges is proposed, which is suitable for mobile machine vision detection. The core of the technology is to obtain high-precision two-dimensional spectrum estimation of crack images by using two-dimensional amplitude and phase estimation method (ab. 2D-APES), and then to enhance the crack information by filtering low-frequency information, so as to realize the automatic recognition of crack targets in images. An industrial-grade drone (DJI Jingwei M200V2) equipped with a high-definition zoom image acquisition system was used to acquire images of the bottom and sides of the bridge of the Minpu Bridge in Shanghai. After locating, magnifying and cutting the apparent crack image of concrete, and then using the above method, the crack automatic identification was realized. Results show that the high-precision non-parametric amplitude spectrum analysis method can adapt to the situation of poor image quality of the UAV, and thus provides a feasible solution for the automatic identification of concrete cracks based on mobile machine vision.

[1]  X. W. Ye,et al.  A review on deep learning-based structural health monitoring of civil infrastructures , 2019 .

[2]  Heng-Da Cheng,et al.  Real-Time Image Thresholding Based on Sample Space Reduction and Interpolation Approach , 2003 .

[3]  Hong Li,et al.  Image Threshold Segmentation Algorithm Based on Histogram Statistical Property , 2014 .

[4]  Toomas Timpka,et al.  Towards productive Knowledge-Based Systems in clinical organizations: a methods perspective , 1994, Artif. Intell. Medicine.

[5]  Jian Li,et al.  Using APES for interferometric SAR imaging , 1998, IEEE Trans. Image Process..

[6]  Jian Li,et al.  An adaptive filtering approach to spectral estimation and SAR imaging , 1996, IEEE Trans. Signal Process..

[7]  S A Velinsky,et al.  Histogram‐Based Approach for Automated Pavement‐Crack Sensing , 1992 .

[8]  Nikhil Katakam,et al.  Pavement Crack Detection System Through Localized Thresholding , 2009 .

[9]  Lei Wang,et al.  Probabilistic Life Prediction for Reinforced Concrete Structures Subjected to Seasonal Corrosion-Fatigue Damage , 2020 .

[10]  I. Harik,et al.  Corrosion Fatigue Crack Propagation Mechanism of High-Strength Steel Bar in Various Environments , 2020 .

[11]  Sung-Han Sim,et al.  Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing , 2017, Sensors.

[12]  Ikhlas Abdel-Qader,et al.  ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .

[13]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[14]  Jeong Ho Lee,et al.  Bridge inspection robot system with machine vision , 2009 .

[15]  Erik G. Larsson,et al.  Fast Implementation of Two-Dimensional APES and CAPON Spectral Estimators , 2002, Multidimens. Syst. Signal Process..

[16]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[17]  Danhui Dan,et al.  Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision , 2019, Measurement.

[18]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[19]  Shuji Hashimoto,et al.  Image Processing Based on Percolation Model , 2006, IEICE Trans. Inf. Syst..