Selective Class-Aware Refinement (SCAR) Method for Microrobot Detection in Ultrasound Images

Microrobots have the power to transform healthcare and the medical sector by improving diagnosis and targeted drug delivery. Real-time detection of microrobots is critical to ensure reliable operation. Ultrasound (US) imaging is used for detection because it is non-ionizing, low-cost, and easy to set up. Using US images is challenging because certain microrobot shapes (classes) remain difficult to detect due to their small size, low contrast, and unstable appearance. This paper proposes Selective Class-Aware Refinement (SCAR), a lightweight post-detection method built on a baseline YOLO detector that boosts its real-time detection capabilities. SCAR improves weak-class detection for visually challenging objects that are small, low-contrast, or unstable, without sacrificing inference speed. To identify weak classes, SCAR is designed to analyze class-specific detection behavior offline. It does so by examining failure statistics and visual difficulty cues. During prediction, SCAR selectively focuses on difficult-to-predict classes using a compact specialist detector applied to adaptive local crops whose size scales with the detected object's dimensions. SCAR limits the refinement to small local regions rather than reprocessing the entire US frame. SCAR preserves near-baseline frame-per-second (FPS) while improving detection. We conducted experiments on the open dataset USMicroMagSet and tested the method on two YOLO baselines and five other state-of-the-art detectors. The results show that SCAR improves weak-class performance with limited computational overhead: it raises weak-class detection accuracy from near-zero to a usable level, improves overall mAP, and maintains consistent performance.