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Updated: Sep 18, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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SAM for Road Object Segmentation: Promising but Challenging.

Alaa Atallah Almazroey¹, Salma Kammoun Jarraya¹, Reem Alnanih¹

¹Faculty of Computing and Information Technology, Computer Science Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Journal of Imaging

|June 25, 2025

Summary

This summary is machine-generated.

The Segment Anything Model (SAM) shows potential for autonomous driving perception but struggles with diverse conditions like changing light and occlusions. Further research is needed to improve its robustness for real-world road object segmentation.

Keywords:

Segment Anything Model (SAM)artificial intelligent autonomous vehicle foundation model road object segmentation

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Deep learning models are vital for autonomous driving perception but face generalization challenges.
Existing models struggle with varying road object appearances due to diverse environmental conditions.

Purpose of the Study:

To comprehensively evaluate the Segment Anything Model (SAM) for zero-shot road object segmentation.
To determine SAM's capabilities and limitations in segmenting road objects under varied real-world autonomous driving conditions.

Main Methods:

Assessed SAM performance on KITTI, BDD100K, and Mapillary Vistas datasets.
Utilized established evaluation metrics to analyze segmentation accuracy.
Focused on zero-shot segmentation without explicit prompts.

Main Results:

SAM demonstrates potential for road object segmentation but exhibits limitations.
Performance is challenged by dynamic environments, illumination changes, and occlusions.
Identified specific areas where SAM excels and where improvements are needed.

Conclusions:

SAM offers a promising foundation for autonomous driving perception systems.
Findings highlight the need for further development to enhance SAM's robustness in complex road scenarios.
Provides insights for future research in foundation models for autonomous driving.