Improved lightweight YOLOv11 for foreign object detection in transmission line inspection

Abstract

In power inspection scenarios, foreign objects are usually difficult to detect because of their small size, complex surrounding backgrounds, and limited computational resources for deployment. To improve detection efficiency under these conditions, this study designs a lightweight YOLOv11-based foreign object detector. StarNet is used to reconstruct the backbone, reducing redundant parameters and computational cost. The C3k2-DSAM module is added to the neck to reinforce fine-grained object features and weaken background interference. In addition, SDIoU is introduced for bounding box regression, which improves the localization of multi-scale targets. Experiments on the self-built dataset show that the improved model obtains 86.5% mAP@0.5, 62.1% mAP@0.5:0.95, and 89.3% Precision at 19.2 GFLOPs, demonstrating competitive accuracy with low computational cost.