TY - GEN
T1 - A Comprehensive Review of Image Segmentation Architectures for Autonomous Navigation and Intelligent Transportation Systems
AU - Panachakel, Jerrin Thomas
AU - Anusha, S. P.
AU - Sabu, Adithi
AU - Arundhati, G. M.
AU - Caren Laurette, R.
AU - Mohan, Chandana
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Segmentation architectures are essential in computer vision, enabling the precise extraction of meaningful information from images and videos by breaking them down into understandable parts. This paper explores six key segmentation architectures: Fully Convolutional Network (FCN), U-Net, DeepLab, YOLO (You Only Look Once) and Mask R-CNN, focusing on their unique methods, strengths, and applications, especially in autonomous navigation. In autonomous vehicles, tasks like lane detection, road segmentation, and identifying objects such as pedestrians and other vehicles rely heavily on semantic segmentation, instance segmentation, and object detection. FCN and U-Net are great at semantic segmentation, making them suitable for understanding the overall scene. DeepLab stands out in tasks that need high-resolution and fine detail. YOLO is preferred when for dynamic environments. This review aims to help researchers and practitioners choose the right architecture based on their specific needs, such as available computational resources, required precision, and the need for real-time processing. By doing so, it seeks to advance the development of smarter transportation systems and better autonomous navigation technologies.
AB - Segmentation architectures are essential in computer vision, enabling the precise extraction of meaningful information from images and videos by breaking them down into understandable parts. This paper explores six key segmentation architectures: Fully Convolutional Network (FCN), U-Net, DeepLab, YOLO (You Only Look Once) and Mask R-CNN, focusing on their unique methods, strengths, and applications, especially in autonomous navigation. In autonomous vehicles, tasks like lane detection, road segmentation, and identifying objects such as pedestrians and other vehicles rely heavily on semantic segmentation, instance segmentation, and object detection. FCN and U-Net are great at semantic segmentation, making them suitable for understanding the overall scene. DeepLab stands out in tasks that need high-resolution and fine detail. YOLO is preferred when for dynamic environments. This review aims to help researchers and practitioners choose the right architecture based on their specific needs, such as available computational resources, required precision, and the need for real-time processing. By doing so, it seeks to advance the development of smarter transportation systems and better autonomous navigation technologies.
KW - Autonomous navigation
KW - Instance segmentation
KW - Semantic segmentation
UR - https://www.scopus.com/pages/publications/105021840036
U2 - 10.1007/978-981-96-8118-1_4
DO - 10.1007/978-981-96-8118-1_4
M3 - Conference contribution
AN - SCOPUS:105021840036
SN - 9789819681174
T3 - Lecture Notes in Civil Engineering
SP - 57
EP - 71
BT - Traffic Management, Operation, Safety and Emerging Technology - Proceedings of TPMDC 2024
A2 - Maji, Avijit
A2 - Velaga, Nagendra Rao
A2 - Debbarma, Solomon
A2 - Nirmale, Sangram Krishna
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Transportation Planning and Implementation Methodologies for Developing Countries, TPMDC 2024
Y2 - 18 December 2024 through 20 December 2024
ER -