Year: 2024
Type: article
Abstract: In computer vision, Convolutional Neural Networks (CNNs) have become a foundation for image analysis. They excel in tasks such as object recognition, classification, and more, semantic segmentation. In order to achieve better accuracy, it is crucial to apply normalization techniques to the network for enhancing overall performance. This paper introduces an innovative approach that incorporates Batch Group Normalization (BGN) into the popular U-Net for binary semantic segmentation, with a particular focus on aerial road detection. Our research primarily focuses on evaluating the BGN-UNet's performance compared to traditional normalization techniques, such as Batch Normalization (BN) and Group Normalization (GN). With a batch size of 2, the U-Net model enhanced with Batch Group Normalization (BGN-UNet) achieves a remarkable Mean IoU of 98.4% in aerial road segmentation, demonstrating its superior accuracy in this task.
Source: Machine Graphics and Vision
Authors: Rayene Doghmane, Karima Boukari
Institution: Badji Mokhtar-Annaba University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408285535