Road Surface Material Detection in Dashcam Images

Date of Award

5-5-2024

Degree Name

M.C.S.

Department

Department of Computer Science

Advisor/Chair

Tam Nguyen

Abstract

The objective of this thesis is to contribute to the advancement of road surface material identification, which has significant implications for applications like enhanced navigation, traction and stability, predictive maintenance, safety considerations, transportation and infrastructure management, and autonomous car driving. In particular, we study how to accurately identify the various materials used in road surfaces, including asphalt, bricks, chip seal, cobblestone, gravel, or concrete. To achieve this goal, we first compiled a comprehensive database of photos acquired from dashcam videos. The data collection process involves obtaining at least 100 photos for each distinct material class. Then, we carefully annotate the surface material for each dashcam photo. After collecting the extensive set of ground truth images, we focus on applying segmentation techniques to effectively isolate the road surfaces from their surrounding contexts such as buildings, vehicles, or pedestrians. Furthermore, we apply the diffusion method to enrich the training data for all surface material classes. We conducted experiments with different deep-learning models. Our results indicate that our proposed work can recognize the road surface material with a high accuracy rate.

Keywords

road, material, surface, detection, deep learning

Rights Statement

Copyright 2024, author

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