Internet of things with bio-inspired co-evolutionary deep-convolution neural-network approach for detecting road cracks in smart transportation

Internet of things (IoT) primarily aims to realize valuable services such as smart homes, smart buildings, and smart transport. To this end, smart applications are faced with different challenges, particularly in the implementation of a smart transportation system, which requires maximum road and travel safety. Road crack detection has been extensively studied and presented with various solutions; however, these approaches are limited by the inhomogeneity in the crack intensity and background complexity, such as a shadow with similar intensity and pavement contrast, which are known obstacles in the accurate prediction of road cracks. To overcome these issues, an IoT system with a bio-inspired deep learning approach was introduced herein for accurate road crack detection. In the proposed approach, transportation images are first collected using a smart mobile sensor, then processed by a bio-inspired self-learning co-evolutionary deep-convolution neural network. The optimized neural networks provide the required framework by analyzing the collected images to detect cracks more accurately. The efficiency of the proposed system was confirmed in different metrics, including the per-pixel accuracy (99.04%), Jaccard index (98.42%), loss error rate (0.03), precision (99.25%), recall (99.24%), and prediction accuracy (99.72%) metrics.

[1]  Jaeseok Kim,et al.  Propagated guided image filtering for edge-preserving smoothing , 2018, Signal Image Video Process..

[2]  Zhili Sun,et al.  Blockchain-Based Dynamic Key Management for Heterogeneous Intelligent Transportation Systems , 2017, IEEE Internet of Things Journal.

[3]  Vishal Pawar,et al.  Internet-of-Things Based Smart Local Bus Transport Management System , 2018, 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).

[4]  Alberto Attilio Brincat,et al.  The Internet of Things for Intelligent Transportation Systems in Real Smart Cities Scenarios , 2019, 2019 IEEE 5th World Forum on Internet of Things (WF-IoT).

[5]  Horst-Michael Groß,et al.  How to get pavement distress detection ready for deep learning? A systematic approach , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[6]  P. Mohamed Shakeel,et al.  An energy persistent Range-dependent Regulated Transmission Communication model for vehicular network applications , 2019, Comput. Networks.

[7]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Qingfu Zhang,et al.  A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[9]  E. Hsiao,et al.  Interactions between the microbiota, immune and nervous systems in health and disease , 2017, Nature Neuroscience.

[10]  Zhun Fan,et al.  Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network , 2018, ArXiv.

[11]  Reza N. Jazar,et al.  Artificial Intelligence and Internet of Things for Autonomous Vehicles , 2019, Nonlinear Approaches in Engineering Applications.

[12]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Aniket Kulkarni,et al.  Pothole Detection System using Machine Learning on Android , 2014 .

[14]  Sotiris Karabetsos,et al.  A Review of Machine Learning and IoT in Smart Transportation , 2019, Future Internet.

[15]  Enas Abdulhay,et al.  Automated multimodal background detection and shadow removal process using robust principal fuzzy gradient partial equation methods in intelligent transportation systems , 2018 .

[16]  N. Aboutabit.  Reduced Featured Based Projective Integral for Road Cracks Detection and Classification , 2020, Pattern Recognition and Image Analysis.

[17]  Umamaheswaran Raman Kumar,et al.  An internet of things based intelligent transportation system , 2014, 2014 IEEE International Conference on Vehicular Electronics and Safety.

[18]  Luis Sánchez-Fernández,et al.  Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving , 2017 .

[19]  David C. Hogg,et al.  Deeper Networks for Pavement Crack Detection , 2017 .

[20]  Li Li,et al.  AUTOMATIC PAVEMENT CRACK RECOGNITION BASED ON BP NEURAL NETWORK , 2014 .

[21]  Amr Tolba,et al.  Optimized feature selection algorithm based on fireflies with gravitational ant colony algorithm for big data predictive analytics , 2018, Neural Computing and Applications.

[22]  Marcelo H. Ang,et al.  Vehicle motion intention reasoning using cooperative perception on urban road , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[23]  Haytham Al-Feel,et al.  Distributed and scalable computing framework for improving request processing of wearable IoT assisted medical sensors on pervasive computing system , 2020, Comput. Commun..

[24]  Andrea Zanella,et al.  Internet of Things for Smart Cities , 2014, IEEE Internet of Things Journal.

[25]  Nick Barnes,et al.  Deep Texture and Structure Aware Filtering Network for Image Smoothing , 2017, ECCV.

[26]  Haifeng Luo,et al.  High-Resolution Aerial Images Semantic Segmentation Using Deep Fully Convolutional Network With Channel Attention Mechanism , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Seshadri Mohan,et al.  Internet of Things for a Smart Transportation System , 2019, Int. J. Interdiscip. Telecommun. Netw..

[28]  Osama Alfarraj,et al.  Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications , 2020, IEEE Access.

[29]  Koray Kavukcuoglu,et al.  Visual Attention , 2020, Computational Models for Cognitive Vision.

[30]  Mohammad R. Jahanshahi,et al.  An innovative methodology for detection and quantification of cracks through incorporation of depth perception , 2011, Machine Vision and Applications.

[31]  Wei Li,et al.  DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Yukihiko Yamashita,et al.  Anisotropic Guided Filtering , 2020, IEEE Transactions on Image Processing.

[33]  Zhuhong Zhang,et al.  Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control , 2008, Appl. Soft Comput..

[34]  Ramón Moreno,et al.  Towards Automatic Crack Detection by Deep Learning and Active Thermography , 2019, IWANN.

[35]  Felix Wortmann,et al.  Autonomously detecting and classifying traffic accident hotspots , 2017, UbiComp/ISWC Adjunct.

[36]  Gilberto Ochoa-Ruiz,et al.  Road Damage Detection Acquisition System based on Deep Neural Networks for Physical Asset Management , 2019, MICAI.

[37]  Hassan Zargarzadeh,et al.  Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network , 2019, Complex..

[38]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Bo Zhou,et al.  Study of Group Route Optimization for IoT Enabled Urban Transportation Network , 2017, 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[40]  Weiwei Sun,et al.  Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM , 2018, IEEE Geoscience and Remote Sensing Letters.