SAFE-YOLO: A Structure-Aware Symbol Detection Method for Electrical Single-Line Diagrams

Electrical single-line diagrams contain symbols with substantial scale variation, thin-line structures, hollow contours, and dense layouts, posing challenges for automatic symbol detection. This study proposes SAFE-YOLO, a YOLOv11s-based detector that integrates local structure modeling, spatial–channel interaction, and multi-scale feature fusion. The structure–frequency-enhanced C3k2 module (C3k2_SF) combines adaptive sampling with frequency-aware channel recalibration. Spatial and channel-wise interactive attention (SICA) introduces bidirectional modulation between spatial and channel branches, while an adopted parallel asymptotic feature fusion network (PAFFN) supports cross-scale feature aggregation and recalibration. Experiments on a dataset of 543 electrical single-line diagrams covering 24 symbol categories showed that SAFE-YOLO achieved an mAP@0.5 of 93.6 ± 0.2%, compared with 88.6 ± 0.1% for YOLOv11s. Measured throughput decreased from 117.6 to 74.1 images/s, while mean latency increased from 8.4 to 13.6 ms per image. These results indicate improved detection accuracy under the adopted evaluation protocol, accompanied by increased inference time. SAFE-YOLO provides a task-oriented option for offline electrical-drawing processing when the additional processing cost is acceptable.