Precise bubble detection is fundamental to process control in chemical engineering and oceanography, yet deploying heavy deep learning models on edge devices is often impractical due to hardware constraints. To bridge this gap, we present LGA-YOLO, a streamlined evolution of YOLO11n optimized for high-speed industrial inspection. By synergizing ghost convolutions with channel shuffling and incorporating a novel Triplet Attention-based feature enhancement, our design systematically eliminates redundancy without compromising discriminative power. The architecture integrates four specialized components—LightShuffleGhostConv, C3k2Ghost, LSPPF, and LightC2fPSA—to maximize efficiency. On our custom side-illuminated bubble dataset, LGA-YOLO maintains a high mAP@50 of 89.0% and mAP@50–95 of 67.0%, with a precision of 90.1% and recall of 85.1%. Crucially, it slashes the parameter count by 67% and FLOPs by 56% compared to the baseline, establishing itself as a viable, high-performance solution for real-time embedded monitoring.
Wenda et al. (2026) studied this question.