Lightweight Small-Object Defect Detection for Industrial Small Transformers Based on an Improved YOLOv12 Network

Appearance defect detection of small industrial transformers is challenging because defects such as bent pins, missing pins, wire breakage, and missing wires are usually small in size and weak in visual features. To improve detection accuracy while maintaining real-time deployment capability, this study proposes an improved lightweight object detection model, named YOLOv12-Optimized, for small transformer quality inspection. First, reparameterized ghost module (RepGhost) re-parameterized modules are introduced into the backbone network to enhance fine-grained feature extraction and reduce computational redundancy. Second, an improved convolutional block attention module (CBAM) is embedded in the neck network to strengthen the response to weak defect features and suppress background interference. Third, an improved wise intersection over union (WIoU) loss function with numerical stability constraints is adopted to improve bounding-box regression robustness for dense small targets. A dedicated small transformer defect dataset was constructed using industrial camera images and data augmentation. Ablation experiments demonstrate that RepGhost, improved CBAM, and improved WIoU each contribute to performance improvement, and their combination achieves the best overall results. Compared with the baseline YOLOv12 model, YOLOv12-Optimized improves mean average precision at an intersection over union threshold of 0.5 (mAP@0.5) from 77.48% to 89.17%, with precision and recall reaching 88.61% and 84.07%, respectively. The model maintains a lightweight structure with 1.98 M parameters and 5.15 giga floating-point operations (GFLOPs), while satisfying real-time inspection requirements. The results indicate that the proposed method effectively balances detection accuracy, model complexity, and industrial applicability, providing a feasible solution for automated appearance quality inspection of small transformers.