LCD-YOLOv11: A Lightweight Object Detection Model for Dry-Type Transformer Winding Process Monitoring

The reliability of dry-type transformers depends heavily on the coil winding process, which currently lacks effective automated monitoring and relies on error-prone manual inspection. Deploying vision-based monitoring on edge devices faces challenges due to complex industrial backgrounds, target occlusions, and strict lightweight processing requirements. To address these issues, we propose LCD-YOLOv11, a lightweight object detection model built upon the YOLOv11n architecture. First, a Lightweight Ghost Coordinate Attention (LGCA) block is introduced into the backbone to reduce computational redundancy while enhancing spatial localization. Second, a CA2RAFE operator replaces standard upsampling in the neck to improve complex boundary reconstruction. Finally, a dynamic detection head (Dy-Detect) is implemented to decouple the parameter scale from the computational cost. Evaluated on a custom dataset, LCD-YOLOv11 achieves an mAP@50 of 0.841, representing a 1.8% improvement over the baseline. Crucially, it significantly reduces the parameter count by 11.58% (to 2.29 M) and the computational load by 20.63% (to 5.0 GFLOPs). LCD-YOLOv11 achieves an effective accuracy–efficiency balance, providing a highly deployable and robust solution for automated compliance verification in industrial manufacturing.