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
Birincil Dil | İngilizce |
---|---|
Konular | Bilgisayar Yazılımı |
Bölüm | Makaleler |
Yazarlar | |
Erken Görünüm Tarihi | 28 Nisan 2023 |
Yayımlanma Tarihi | 30 Nisan 2023 |
Gönderilme Tarihi | 4 Eylül 2022 |
Kabul Tarihi | 24 Şubat 2023 |
Yayımlandığı Sayı | Yıl 2023Cilt: 6 Sayı: 1 |
Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science