Earticle

Bubble defects reduce the reliability of PCB coatings. Accurate, real-time detection of small bubbles is crucial. In this study, we present a fast and lightweight YOLOv8n-based detector. It improves the average precision of small bubbles by approximately 12 points. It also increases mAP50 from 0.637 to 0.697 in 49.1 milliseconds per image. This is a practical solution that supports quality control of micro-defects. NAMAttention reweights channel and spatial features. C2F fuses backbone and neck features. The P2 layer expands the receptive field for micro-bubbles. During training, size-aware loss emphasizes small bubbles. Defect-balanced sampling addresses class imbalance. Glare synthesis improves robustness to illumination variations. During inference, Soft-NMS reduces false positives caused by overlapping boxes. Our contribution lies in the organic integration of a learning strategy specialized for micro-defects and architectural improvements. At the same time, we maintain a lightweight structure and improve detection performance and latency. We quantitatively verified the utility and practical feasibility of each component through module-bymodule erasure experiments. We also performed throughput evaluations in field-deployment scenarios. This approach is effective for PCB quality control and suitable for detecting micro-defects in similar manufacturing processes.