Correctly determining the driving area and pedestrians is crucial for intelligent vehicles to reduce fatal road accidents risk. But these are challenging tasks in the computer vision field. Various weather, road conditions, etc., make them difficult. This paper presents a vision-based road segmentation and pedestrian detection system. First, the roads are segmented using a deep learning based consecutive triple filter size (CTFS) approach. Then, pedestrians on the segmented roads are detected using the transfer learning approach. The CTFS approach can create feature maps for small and big features. The proposed system is a reliable, low-cost road segmentation and pedestrian detection system for intelligent vehicles.
Pedestrian detection road segmentation convolutional neural networks intelligent vehicles Transfer Learning
Primary Language | English |
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Subjects | Computer Software |
Journal Section | Articles |
Authors | |
Early Pub Date | April 28, 2023 |
Publication Date | April 30, 2023 |
Submission Date | September 4, 2022 |
Acceptance Date | February 24, 2023 |
Published in Issue | Year 2023Volume: 6 Issue: 1 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License