Roadway Inspection System
DOI:
https://doi.org/10.32628/IJSRSET2411259Keywords:
Speed breaker detection, Roadway inspection, Deep learning, Convolutional Neural Network, Computer vision, Pothole detection, Automated inspection, Image recognition, Deep learning for infrastructureAbstract
Traditional road inspections are manual processes, prone to human error and inefficiencies. This paper presents a novel approach for automated roadway inspection using a Convolutional Neural Network (CNN) model. Our system leverages computer vision techniques to detect potholes and speed breakers on road surfaces from images. We developed a CNN model trained on a comprehensive dataset of road images containing various pothole and speed breaker types, lighting conditions, and road backgrounds. The model achieved an accuracy of 93% in detecting these road defects, demonstrating the effectiveness of deep learning for automated roadway inspections. This system has the potential to significantly improve the efficiency and objectivity of road inspections, leading to faster repairs and improved road safety
Downloads
References
Real-time Pothole Detection and Speed Breaker Detection Using Image Processing Techniques IRJET: https://www.irjet.net/archives/V10/i9/IRJET-V10I9106.pdf
Automatic pothole and speed breaker detection using android system ResearchGate: https://www.researchgate.net/publication/305907592_Auto matic_pothole_and_speed_breaker_detection_using_androi d_system
Smartphone-Based Pothole and Speed-Breaker Detection Springer Link: https://link.springer.com/chapter/10.1007/978-981-99- 7711-6_32 2.
IoT based Automatic Detection of Pot Holes and Speed Breaker MappingIJRESM: https://www.ijresm.com/Vol.2_2019/Vol2_Iss5_May19/IJ RESM_V2_I5_112.pdf 4.
Deep Learning for Automatic Road Crack Detection: A Survey IEEE Transactions on Intelligent Transportation Systems: https://ieeexplore.ieee.org/document/9441167 6.
A Survey of Deep Learning Techniques for Road Object Detection IEEE Access: https://ieeexplore.ieee.org/document/8825470 8.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks arXiv: https://arxiv.org/abs/1506.01497
YOLO: You Only Look Once: Unified Real-Time Object Detection arXiv: https://arxiv.org/abs/1506.02640
SD: Single-Shot MultiBox Detector arXiv: https://arxiv.org/abs/1512.02325
Transfer Learning for Visual Recognition arXiv: https://link.springer.com/article/10.1007/s11633-020-1273-9
VGG16: Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv: https://arxiv.org/abs/1409.1556
Deep Residual Learning for Image Recognition arXiv: https://arxiv.org/abs/1512.03385 13. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications arXiv: https://arxiv.org/abs/1704.04861
Edge Computing for Intelligent Transportation Systems: A Survey IEEE Access: https://ieeexplore.ieee.org/document/10133894/
Context-Aware Driver Assistant Systems IEEE Transactions on Intelligent Transportation Systems:
Privacy-Preserving Data Aggregation in Intelligent Transportation SystemsIEEE Transactions on Intelligent Transportation Systems: https://ieeexplore.ieee.org/iel7/6488907/6702522/09759501.pdf
Real-time Collaborative Mapping for Indoor Pedestrian Navigation IEEE Transactions on Intelligent Transportation Systems: https://ieeexplore.ieee.org/document/10017666/
A Survey of Urban Traffic Management Systems Proceedings of the IEEE: https://www.researchgate.net/publication/292175972_A_Surve y_on_Urban_Traffic_Management_System_Using_Wir eless_Sensor_Networks
Sustainable Urban Infrastructure Management Procedia Engineering: https://www.sciencedirect.com/science/article/pii/S0921344916301914
Smart Cities: A Survey on Technologies, Applications and Future Challenges Computer Communications
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
