# DCDW-YOLOv11: An Intelligent Defect-Detection Method for Key Transmission-Line Equipment

## TL;DR

This paper introduces DCDW-YOLOv11, an improved model for detecting defects in transmission-line equipment with higher accuracy and reliability in complex environments.

## Contribution

The novel DCDW-YOLOv11 model integrates deformable convolution and attention modules for better defect detection in transmission-line equipment.

## Key findings

- DCDW-YOLOv11 achieved 94.4% accuracy, 92.8% recall, and 96.3% mAP on the defect detection dataset.
- The model outperformed YOLOv11 by 2.8%, 7.0%, and 4.4% in accuracy, recall, and mAP respectively.
- It provides reliable defect detection for transmission-line equipment in complex scenarios.

## Abstract

The detection of defects in key transmission-line equipment under complex environments often suffers from insufficient accuracy and reliability due to background interference and multi-scale feature variations. To address this issue, this paper proposes an improved defect detection model based on YOLOv11, named DCDW-YOLOv11. The model introduces deformable convolution C2f_DCNv3 in the backbone network to enhance adaptability to geometric deformations of targets, and incorporates the convolutional block attention module (CBAM) to highlight defect features while suppressing background interference. In the detection head, a dynamic head structure (DyHead) is adopted to achieve cross-layer multi-scale feature fusion and collaborative perception, along with the WIoU loss function to optimize bounding box regression and sample weight allocation. Experimental results demonstrate that on the transmission-line equipment defect dataset, DCDW-YOLOv11 achieves an accuracy, recall, and mAP of 94.4%, 92.8%, and 96.3%, respectively, representing improvements of 2.8%, 7.0%, and 4.4% over the original YOLOv11, and outperforming other mainstream detection models. The proposed method can provide high-precision and highly reliable defect detection support for intelligent inspection of transmission lines in complex scenarios.