# Classification of Potholes Based on Surface Area Using Pre-Trained Models of Convolutional Neural Network

arXiv: https://arxiv.org/abs/2309.17426 · HTML: https://mlsanity.com/paper/2309.17426/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 79 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Pothole Detection Based on Disparity Transformation and Road Surface Modeling](https://mlsanity.com/paper/1908.00894/) (2019)
- [Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation](https://mlsanity.com/paper/2012.10802/) (2020)
- [Road Damage Detection Based on Unsupervised Disparity Map Segmentation](https://mlsanity.com/paper/1910.04988/) (2019)
- [Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms](https://mlsanity.com/paper/2109.02711/) (2021)
- [We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation](https://mlsanity.com/paper/2008.06840/) (2020)
- [Crack-pot: Autonomous Road Crack and Pothole Detection](https://mlsanity.com/paper/1810.05107/) (2018)
- [Learning to Automatically Catch Potholes in Worldwide Road Scene Images](https://mlsanity.com/paper/2105.07986/) (2021)
- [Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection](https://mlsanity.com/paper/2112.13082/) (2021)

## Abstract (arXiv)

Potholes are fatal and can cause severe damage to vehicles as well as can cause deadly accidents. In South Asian countries, pavement distresses are the primary cause due to poor subgrade conditions, lack of subsurface drainage, and excessive rainfalls. The present research compares the performance of three pre-trained Convolutional Neural Network (CNN) models, i.e., ResNet 50, ResNet 18, and MobileNet. At first, pavement images are classified to find whether images contain potholes, i.e., Potholes or Normal. Secondly, pavements images are classi-fied into three categories, i.e., Small Pothole, Large Pothole, and Normal. Pavement images are taken from 3.5 feet (waist height) and 2 feet. MobileNet v2 has an accuracy of 98% for detecting a pothole. The classification of images taken at the height of 2 feet has an accuracy value of 87.33%, 88.67%, and 92% for classifying the large, small, and normal pavement, respectively. Similarly, the classification of the images taken from full of waist (FFW) height has an accuracy value of 98.67%, 98.67%, and 100%.