MFIA-YOLO: A Small-Object Defect Detection Model for Transmission Line Spacers

UAV inspection images of transmission line spacers often contain small-scale defect targets, complex backgrounds, and weak fine-grained features. We propose MFIA-YOLO, a small-object defect detection model for spacer defects. Using the lightweight YOLOv8n variant as the baseline, we introduce a Multi-scale Spatial Heterogeneous Convolution (MSHC) into the backbone network. This module enhances the extraction of multi-scale features from spacer defect targets. Before the SPPF module, we further construct a Feature Complementary module (FCM). The FCM embeds shallow spatial location information into deep semantic features, thereby alleviating spatial information degradation during small-object detection. In the detection head, a C2f_IAFF module is adopted to adaptively fuse features at different scales through iterative attention-based feature fusion. This design improves the representation of defect targets in complex scenes. In addition, a transmission line spacer defect dataset is constructed from UAV inspection images collected in Xilingol League, Inner Mongolia. Experimental results showed that MFIA-YOLO achieved an mAP50 of 96.53% and an mAP50–95 of 83.50%. Compared with representative YOLO-series models, MFIA-YOLO achieved a better balance between detection accuracy and model complexity. These results demonstrate its effectiveness for accurate spacer defect detection in transmission lines.