Research on Deep Learning-based Image Semantic Segmentation and Scene Understanding

Abstract

This research investigates the intricate domain of deep learning-based image semantic segmentation and scene understanding. The fundamentals of image semantic segmentation are explored, tracing the evolution from traditional methods to the emergence of deep learning techniques. Deep learning architectures for semantic segmentation are thoroughly reviewed, encompassing popular CNNs architectures like U-Net, FCNs, and SegNet, along with their respective advantages and drawbacks. Furthermore, recent advancements and novel architectures aimed at improving segmentation performance are scrutinized, highlighting the integration of attention mechanisms and the development of encoder-decoder architectures with skip connections. Datasets and Evaluation Metrics crucial for benchmarking and assessing the efficacy of semantic segmentation models are also examined. By addressing these facets comprehensively, this research aims to contribute to the ongoing advancement of deep learning methodologies in image analysis, fostering enhanced scene understanding and paving the way for more robust computer vision systems.