# Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach

arXiv: https://arxiv.org/abs/2510.03797 · HTML: https://mlsanity.com/paper/2510.03797/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)
- [Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market](https://mlsanity.com/paper/2208.08300/) (2022)
- [Plant Disease Detection using Region-Based Convolutional Neural Network](https://mlsanity.com/paper/2303.09063/) (2023)

## Built on by (most cited first, 9 of 9)

- [Detecting Unauthorized Vehicles using Deep Learning for Smart Cities: A Case Study on Bangladesh](https://mlsanity.com/paper/2510.26154/) (2025)
- [Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks](https://mlsanity.com/paper/2601.01099/) (2026)
- [Evaluation of Convolutional Neural Network For Image Classification with Agricultural and Urban Datasets](https://mlsanity.com/paper/2601.01393/) (2026)
- [A Comparative Study of Custom CNNs, Pre-trained Models, and Transfer Learning Across Multiple Visual Datasets](https://mlsanity.com/paper/2601.02246/) (2026)
- [Experimental Comparison of Light-Weight and Deep CNN Models Across Diverse Datasets](https://mlsanity.com/paper/2601.03463/) (2026)
- [Comparative Analysis of Custom CNN Architectures versus Pre-trained Models and Transfer Learning: A Study on Five Bangladesh Datasets](https://mlsanity.com/paper/2601.04352/) (2026)
- [Performance Analysis of Image Classification on Bangladeshi Datasets](https://mlsanity.com/paper/2601.04397/) (2026)
- [Training a Custom CNN on Five Heterogeneous Image Datasets](https://mlsanity.com/paper/2601.04727/) (2026)
- [Reliable Deep Learning for Small-Scale Classifications: Experiments on Real-World Image Datasets from Bangladesh](https://mlsanity.com/paper/2601.11911/) (2026)

## Abstract (arXiv)

Urban safety and infrastructure maintenance are critical components of smart city development. Manual monitoring of road damages is time-consuming, highly costly, and error-prone. This paper presents a deep learning approach for automated road damage and manhole detection using the YOLOv9 algorithm with polygonal annotations. Unlike traditional bounding box annotation, we employ polygonal annotations for more precise localization of road defects. We develop a novel dataset comprising more than one thousand images which are mostly collected from Dhaka, Bangladesh. This dataset is used to train a YOLO-based model for three classes, namely Broken, Not Broken, and Manhole. We achieve 78.1% overall image-level accuracy. The YOLOv9 model demonstrates strong performance for Broken (86.7% F1-score) and Not Broken (89.2% F1-score) classes, with challenges in Manhole detection (18.2% F1-score) due to class imbalance. Our approach offers an efficient and scalable solution for monitoring urban infrastructure in developing countries.