Automated detection of Martian dust storms is critical for analyzing planetary climate dynamics, yet segmentation remains challenging due to diffuse storm boundaries and data artifacts. This study presents a Convolutional Block Attention Module-enhanced (CBAM-enhanced) U-Net architecture for dust storm segmentation using Mars Reconnaissance Orbiter (MRO) MARCI Mars Daily Global Maps (MDGMs) from the Mars Dust Activity Database (MDAD v1.1). The approach combines attention-driven feature refinement with class-imbalance mitigation and a patching strategy to handle missing data in global maps. The model achieves 0.6502 Intersection over Union (IoU) and 0.6883 Dice scores on MDAD data, outperforming baseline U-Net by 3%, while using 8x fewer parameters (1.95M vs 23M) in comparison to state-of-the-art methods, significantly reducing computational costs. Ablation experiments confirm CBAM reduces false positives and preserves fine boundaries; case studies show the model, in some cases, detects sub-visual dust features missed in ground truth annotations, suggesting potential utility for discovering marginal atmospheric phenomena. This work establishes an efficient framework for processing planetary image data while balancing accuracy and computational practicality.
