Towards automatic inspection: crack recognition based on Quadrotor UAV-taken images

Building inspection searching for superficial defects, such as cracks, is a vital task because such damages cause economic losses or put at risk the integrity of people. For this reason, different ways to reduce the costs and risks through the use of robotic systems that allow make inspections have been studied. Among these robotic systems, we have the unmanned aerial vehicles (UAV) that allow reaching difficult access places permitting better inspection. In this work, we propose using convolutional neuronal networks for crack recognition from images captured by an UAV. To carry out the training task of the network, a database of cracks in walls was built from images collected from the Internet. The training of the network prompted encouraging results with a 95% accuracy over the training set. Experimental results of crack recognition in images were carried out validating the application of the proposal.

[1]  Eung-kon Kim,et al.  Building crack inspection using small UAV , 2015, 2015 17th International Conference on Advanced Communication Technology (ICACT).

[2]  Fan Meng,et al.  Automatic Road Crack Detection Using Random Structured Forests , 2016, IEEE Transactions on Intelligent Transportation Systems.

[3]  M. Moghavvemi,et al.  Modelling and PID controller design for a quadrotor unmanned air vehicle , 2010, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).

[4]  Rogelio Lozano,et al.  Real-time stabilization and tracking of a four rotor mini-rotorcraft , 2003 .

[5]  Ravi Vaidyanathan,et al.  Hand gesture recognition with convolutional neural networks for the multimodal UAV control , 2017, 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS).

[6]  Rogelio Lozano,et al.  Chattering-Free Sliding Mode Altitude Control for a Quad-Rotor Aircraft: Real-Time Application , 2014, J. Intell. Robotic Syst..

[7]  Tran Hiep Dinh,et al.  Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles , 2017, ArXiv.

[8]  Yimin D. Zhang,et al.  Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[9]  Ahmet Bahaddin Ersoz,et al.  Crack identification for rigid pavements using unmanned aerial vehicles , 2017 .

[10]  Zijun Zhang,et al.  Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-Taken Images , 2017, IEEE Transactions on Industrial Electronics.

[11]  Xianbin Cao,et al.  Power line detection via background noise removal , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[12]  Jie Cao,et al.  Application of deep learning and unmanned aerial vehicle technology in traffic flow monitoring , 2017, 2017 International Conference on Machine Learning and Cybernetics (ICMLC).

[13]  Rogelio Lozano,et al.  Real-time stabilization and tracking of a four-rotor mini rotorcraft , 2004, IEEE Transactions on Control Systems Technology.

[14]  Lin Lei,et al.  Fast vehicle detection in UAV images , 2017, 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP).

[15]  Rogelio Lozano,et al.  Unmanned Aerial Vehicles Embedded Control , 2013 .

[16]  Carlos Eduardo Pereira,et al.  Embedded Image Processing Systems for Automatic Recognition of Cracks using UAVs , 2015 .

[17]  Sidney Nascimento Givigi,et al.  Automatic Crack Detection and Measurement Based on Image Analysis , 2016, IEEE Transactions on Instrumentation and Measurement.

[18]  Rishi Gupta,et al.  Health Monitoring of Civil Structures with Integrated UAV and Image Processing System , 2015 .

[19]  Fan Xi,et al.  Detection crack in image using Otsu method and multiple filtering in image processing techniques , 2016 .

[20]  Jizhong Xiao,et al.  Deep Concrete Inspection Using Unmanned Aerial Vehicle Towards CSSC Database , 2017 .