Deep Learning-Based Road Spill Detection Technology Southeast University

Format:

Book

Conference/Event

Author/Creator:

Gao, Xiaofei, author.

Conference Name:

2024 International Conference on Smart Transportation Interdisciplinary Studies (2024-12-13 : Nanjing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Rapid identification and cleanup of road debris are essential for enhancing traffic safety and ensuring unobstructed road conditions. Traditional detection methods often face challenges in accurately identifying debris in complex environments with varying light and weather conditions. To address these limitations, this study proposes a deep learning-based road debris detection method designed for improved accuracy and robustness. First, road images are processed using a semantic segmentation approach to remove background information, isolating only the drivable areas. This segmented region is then subjected to further object detection to filter out typical non-debris objects, such as vehicles, pedestrians, and non-motorized vehicles, thereby retaining a focused image that only contains potential debris or spill objects. Lastly, the processed image is compared to a baseline image to detect differences and identify road debris with high precision. Through these steps, the proposed method achieves accurate detection of road debris even in challenging environmental conditions. Experimental results validate the model's effectiveness, indicating that it provides a reliable solution for debris recognition, ultimately offering robust technical support for intelligent traffic management systems aimed at promoting safer, cleaner, and more efficient roads

Notes:

Vendor supplied data

Publisher Number:

2025-01-7130

Access Restriction:

Restricted for use by site license