Abstract: To address the issues of severe background interference, large variations in defect target scales, inconspicuous features of small-scale defects, and progressive loss of edge information in deep networks during insulator defect detection in UAV inspection scenarios, and given the favorable balance between real-time performance and parameter count of YOLOv11n, this paper proposes an improved insulator defect detection method based on YOLOv11n to enhance detection accuracy and robustness under complex background conditions. This method performs collaborative optimization of the network structure from three aspects, namely feature enhancement, feature fusion, and feature extraction, thereby improving the model's perception and recognition capabilities for insulator defect targets in complex scenes while maintaining lightweight characteristics.The proposed model incorporates three core optimization modules. First, to alleviate complex background interference and enhance fine-grained defect representation capability, a Dynamic Dual-Domain Feature Enhancement Module (DDFEM) is designed in the feature extraction stage of the backbone network, establishing a collaborative working mechanism between a global semantic branch and a local detail branch. The global branch establishes long-range spatial dependencies through bidirectional global pooling and cross-dimensional matrix interaction, achieving global semantic modeling with low computational complexity. The local branch extracts fine-grained texture features such as flashover traces and damage using multi-branch depthwise separable convolutions, combined with a dynamic attention fusion mechanism for adaptive recalibration of local features, thereby achieving complementary enhancement of global semantic information and local detail information. Second, to address the progressive weakening of edge detail information during deep semantic feature enhancement, a Sobel-Edge-Guided Weighted Fusion Module (SEGWF) is designed. This module explicitly extracts edge information from shallow features using the Sobel operator and dynamically fuses edge structural information with deep semantic features through a channel-wise weighted fusion mechanism, thereby enhancing the model's perception of key structural features such as damaged edges and contours of insulators, and improving small target detection performance under complex backgrounds. Finally, to enhance multi-scale target feature extraction capability and address the fixed receptive field of traditional convolutions that struggles to adapt to defects of different scales, a Receptive-Field Attention Convolution (RFAConv) mechanism is introduced into the backbone network to replace traditional convolutional structures. This mechanism adaptively adjusts the receptive field response range according to the feature distribution of different spatial regions, thereby improving the model's feature extraction capability for insulator defects at different scales and enhancing multi-scale target detection performance.To verify the effectiveness of the proposed method, experimental studies are conducted on a self-constructed insulator defect dataset, with comparisons against mainstream object detection models. Experimental results show that the proposed method achieves 92.2% mAP@0.5, representing a 4.9 percentage point improvement over the original YOLOv11n.Precision increases from 89.7% to 93.0% with a 3.3 percentage point improvement, while Recall increases from 79.6% to 88.3% with an 8.7 percentage point improvement, demonstrating that the proposed method effectively reduces the miss detection rate and improves defect detection accuracy in complex scenes. Furthermore, to validate the generalization capability of the model, tests are conducted on the public insulator defect dataset IDID. Experimental results show that the proposed method achieves a Precision of 89.6%, a Recall of 79.8% and an mAP@0.5 of 88.9%. It obtains the optimal mAP@0.5 among all comparative models, with an improvement of 1.9 percentage points over the original YOLOv11n, which verifies its strong cross-dataset generalization ability and stable detection performance. Meanwhile, the proposed model only has 3.12M parameters and a computational cost of 7.6 GFLOPs, maintaining low computational complexity while boosting detection accuracy. In conclusion, the improved YOLOv11n algorithm proposed in this paper can effectively complete the task of insulator defect detection under complex backgrounds.

摘要： 针对无人机巡检场景下绝缘子缺陷检测过程中存在背景干扰严重、缺陷目标尺度变化大、小尺度缺陷特征不明显以及深层网络边缘信息逐渐丢失等问题，鉴于YOLOv11n在实时性与参数量间的良好平衡，本文以其为基础框架进行改进，提出一种基于改进YOLOv11n的绝缘子缺陷检测方法，以提升模型在复杂背景条件下对绝缘子缺陷的检测精度与鲁棒性。该方法从特征增强、特征融合以及特征提取三个层面对网络结构进行协同优化，在保证模型轻量化特性的同时提高其对复杂场景中绝缘子缺陷目标的感知与识别能力。本文模型包含三项核心优化模块。首先，为缓解复杂背景干扰、强化细粒度缺陷表征能力，在骨干网络的特征提取阶段设计动态双域特征增强模块(Dynamic Dual-Domain Feature Enhancement Module, DDFEM)，构建全局语义分支与局部细节分支协同工作机制。其中，全局分支利用双方向全局池化与跨维度矩阵交互建立长距离空间依赖关系，实现低计算复杂度下的全局语义建模；局部分支采用多分支深度可分离卷积结构提取闪络损伤、破损等细粒度纹理特征，并结合动态注意力融合机制对局部特征进行自适应重标定，从而实现全局语义信息与局部细节信息的互补增强。其次，针对深层语义特征增强过程中边缘细节信息逐渐弱化的问题，设计Sobel边缘引导加权融合模块(Sobel-Edge-Guided Weighted Fusion Module, SEGWF)，利用Sobel算子从浅层特征中显式提取边缘信息，并通过通道加权融合机制将边缘结构信息与深层语义特征进行动态融合，以增强模型对绝缘子破损边缘、轮廓等关键结构特征的感知能力，提高复杂背景下的小目标检测性能。最后，为增强多尺度目标的特征提取能力，针对传统卷积感受野固定、难以适应不同尺度缺陷的问题，在骨干网络中引入感受野注意力卷积机制(Receptive-Field Attention Convolution, RFAConv)替换传统卷积结构，该机制能根据不同空间区域的特征分布自适应调整感受野响应范围，从而提升模型对不同尺度绝缘子缺陷的检测性能。为验证所提方法的有效性，在自建绝缘子缺陷数据集上开展实验研究，并与主流目标检测模型进行对比。实验结果表明，本文方法取得92.2%的mAP@0.5，相较于原始YOLOv11n提高4.9个百分点；Precision由89.7%提升至93.0%，提高3.3个百分点；Recall由79.6%提升至88.3%，提高8.7个百分点，表明所提方法能够有效降低漏检率并提升复杂场景下的缺陷检测精度。同时，为进一步验证模型的泛化能力，在公开绝缘子缺陷数据集IDID上进行测试。结果显示，本文方法Precision为89.6%，Recall为79.8%，mAP@0.5为88.9%，在所有对比模型中取得最高的mAP@0.5指标，相较于原始YOLOv11n提升1.9个百分点，体现出较好的跨数据集泛化能力和稳定检测性能。同时，本文模型参数量仅为3.12M，计算量为7.6 GFLOPs，在精度提升的前提下保持了较低的计算复杂度。综上，本文所提出的改进YOLOv11n算法能够有效完成复杂背景下的绝缘子缺陷检测任务。