Benchmarking automated MIC detection: the AI-ready space biology SEM dataset and advanced detection methods

Microbial-induced corrosion (MIC) severely threatens the structural integrity of metals in high-stakes environments, from aerospace to terrestrial infrastructure. Rapid, reliable detection is essential for informed mitigation and maintenance. The paper presents a computer-vision pipeline that establishes a new benchmark for MIC region segmentation in scanning electron microscopy (SEM) imagery. Central to this advancement is our expanded, expertly annotated dataset of 331 SEM images of MIC on stainless steel, the largest and, to our knowledge, first AI-ready segmentation dataset to include spaceflight samples to date. Leveraging this resource, we rigorously benchmark both classical and deep learning methods and introduce two deep learning architectures: an enhanced SAM2 and a novel Prompt- and Heatmap-Guided FPN-based Lightweight Segmentation Model (Lightweight PH-FPNSeg). Among these contributions, the most significant is the release of the curated, AI-ready MIC-SEM dataset with spaceflight samples, which we position as a reference benchmark for future work. The enhanced SAM2 and Lightweight PH-FPNSeg provide strong baselines on this benchmark. Both models deliver state-of-the-art results, achieving average Dice and IoU scores of 82% and 70%, respectively—substantially surpassing prior approaches.