@inproceedings{db9656c83f294ff29cecffafed7e1e55,
title = "Deep Learning-Based Road Defect Detection Using an Improved YOLOv8 Model",
abstract = "This study proposes a model with YOLOv8x as the core framework and targeted optimization to achieve low-cost road defect detection and classification.First, the diversity and robustness of the dataset were enriched through Data Augmentation technology, which effectively improved the model's ability to recognize different types of road defect after training.Secondly, for Convolutional Neural Network (CNN), the Feature Pyramid structure is introduced in this study, which enables the model to capture and identify the subtle defect features in road images more accurately, further improving the accuracy and effect of detection.The experimental results show that the improved scheme has achieved obvious results, the F1 score increased from 0.58350 of the traditional YOLOv8 to 0.62936.The model can identify various types of damaged pavement more comprehensively and accurately.It provides a more reliable and effective solution for road defect detection in various road conditions.",
keywords = "Centralized Feature Pyramid, Inverted Residual Mobile Block, Object detection, Road defect detection, YOLOv8x",
author = "Keyi Liu and Huizhong Zheng and Xiangjian He",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2025 International Workshop on Advanced Imaging Technology, IWAIT 2025 ; Conference date: 06-01-2025 Through 08-01-2025",
year = "2025",
doi = "10.1117/12.3057869",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masayuki Nakajima and Chuan-Yu Chang and Chia-Hung Yeh and Jae-Gon Kim and Kemao Qian and Lau, {Phooi Yee}",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2025",
address = "United States",
}