Inhaltszusammenfassung

This survey offers a comprehensive analysis of challenges encountered when employing large-scale datasets for deep learning-based semantic segmentation, an area with significant implica- tions for industries such as autonomous driving, precision agriculture, and medical imaging. Through a systematic review of 94 papers from Papers with Code, we identified 32 substantial challenges, which we categorized into six key areas: Data Quality and Quantity, Data Preprocessing, Resource Constraints, Da...This survey offers a comprehensive analysis of challenges encountered when employing large-scale datasets for deep learning-based semantic segmentation, an area with significant implica- tions for industries such as autonomous driving, precision agriculture, and medical imaging. Through a systematic review of 94 papers from Papers with Code, we identified 32 substantial challenges, which we categorized into six key areas: Data Quality and Quantity, Data Preprocessing, Resource Constraints, Data Management and Privacy, Generalization, and Data Compatibility. By identifying and explicating these challenges, our research provides a crucial reference point for future studies aiming to address these issues and enhance the performance of deep learning models for semantic segmentation. Future work will focus on leveraging AI and semantic technologies to provide solutions to these challenges.» weiterlesen» einklappen

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DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Informatik