# HazardNet: Road Debris Detection by Augmentation of Synthetic Models

**Authors:** Tae Eun Choe, Jane Wu, Xiaolin Lin, Karen Kwon, Minwoo Park  
**arXiv:** [2303.07547](https://arxiv.org/abs/2303.07547)  
**Date:** 2023-03-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.07547v1/

## Key points

- Introduction of innovative algorithm for detecting unseen road debris using synthetic models
- Importance of early detection of road debris for safety of autonomous and assisted driving systems
- Challenges in building an effective detector: difficulty in collecting data on road debris and variability in types of debris
- Proposed approach involving few-shot learning, semantic augmentation, and domain randomization techniques
- Use of deep neural network HazardNet to learn semantic meaning of road debris and detect previously unseen types
- High accuracy demonstrated in detecting real road debris even when trained solely on synthetic objects within augmented images
- Authors: Tae Eun Choe, Jane Wu, Xiaolin Lin, Karen Kwon, Minwoo Park

## Summary

The paper titled "HazardNet: Road Debris Detection by Augmentation of Synthetic Models" introduces an innovative algorithm for detecting unseen road debris using a limited set of synthetic models. Detecting road debris early is crucial for ensuring the safety of autonomous and assisted driving systems. However, the development of a robust road debris detection model has not been extensively explored in existing literature. The authors address this gap by highlighting two primary challenges associated with building an effective detector: the difficulty in collecting data on road debris and the wide range of variability in types of debris. To overcome these challenges, the authors propose a novel approach centered around few-shot learning and leveraging semantic augmentation and domain randomization techniques to enhance real road images with synthetic models. This allows their deep neural network, HazardNet, to learn the semantic meaning of road debris and detect previously unseen types. The results demonstrate high accuracy in detecting real road debris even when trained solely on synthetic objects within augmented images. Authored by Tae Eun Choe, Jane Wu, Xiaolin Lin, Karen Kwon, and Minwoo Park, this research contributes valuable insights towards advancing the field of road debris detection through innovative use of synthetic models and deep learning techniques.