MFSCNN: APPENDING A MASKED BRANCH TO FAST-SCNN TO IMPROVE ROAD MARKING EXTRACTION ON SPARSE MLS POINT CLOUD-DERIVED IMAGES

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLVIII-1/W2-2023; pp. 829 - 834
• Main Authors: Lagahit, M. L. R., Matsuoka, M.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 01.01.2023; Copernicus Publications
• Subjects: Archives & records; Artificial neural networks; Automobiles; Autonomous cars; Convolution; Datasets; Deep learning; Digital mapping; Digital twins; Driving ability; High definition; Information science; Lidar; Low cost; Machine learning; Mapping; Marking; Neural networks; Photogrammetry; Remote sensing; Roads & highways; Sensors
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-1-W2-2023-829-2023

With the rise of self-driving cars, an increasing number of vehicles are equipped with low-cost light detection and ranging (LiDAR) sensors that could potentially serve as a massive mobile mapping resource, particularly for jobs that require multiple and frequent scanning, such as maintaining dynamic high-definition maps or digital twins. However, low-cost LiDAR sensors produce sparser point clouds during scanning which can make deep learning techniques for the automatic retrieval of features difficult like extracting road markings. In this work, we aim to improve the performance of a convolutional neural network (CNN) model for road marking extraction from sparse mobile LiDAR scanning (MLS) point cloud-derived images. We propose the modification of the Fast-SCNN model structure by adding a 2D convolution branch with masking in the feature fusion step: MFSCNN. To retain speed we only use MFSCNN to boost model training and still utilize Fast-SCNN for inference. Our results indicate potential, with a 4.6% increase in mean f1-score and an 8% decrease in uncertainty for the road marking class after multiple trials. Additionally, this research aims to support and increase research interest in lower-cost LiDARs for mobile mapping.