Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines
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Abstract (arXiv)
Road transport infrastructure is critical for safe, fast, economical, and reliable mobility within the whole country that is conducive to a productive society. However, roads tend to deteriorate over time due to natural causes in the environment and repeated traffic loads. Pavement Distress (PD) detection is essential in monitoring the current conditions of the public roads to enable targeted rehabilitation and preventive maintenance. Nonetheless, distress detection surveys are still done via manual inspection for developing countries such as the Philippines. This study proposed the use of deep learning for two ways of recording pavement distresses from 2D RGB images - detection and segmentation. YOLOv4 is used for pavement distress detection while DeepLabv3 is employed for pavement distress segmentation on a small dataset of pavement images in the Philippines. This study aims to provide a basis to potentially spark solutions in building a cheap, scalable, and automated end-to-end solution for PD detection in the country.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 60 references that are in the map. First posted on arXiv 11 Mar 2021.
Builds on
18 of 60 references.
References · 18
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
- 2018YOLOv3: An Incremental Improvementcited by 2,652
- 2018CBAM: Convolutional Block Attention Modulecited by 2,463
- 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2016YOLO9000: Better, Faster, Strongercited by 1,429
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
- 2018Path Aggregation Network for Instance Segmentationcited by 796
- 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
6 more
- 2014Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFscited by 552
- 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
- 2018Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphonecited by 44
- 2019Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholdingcited by 33
- 2016A Survey of Semantic Segmentationcited by 30
- 2020Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Networkcited by 2
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.