用无人机图像检测输电线路绝缘子缺陷，提升小样本、不平衡数据下的识别准确率。
Attention-Guided Autoencoder Fusion for Insulator Defect Detection Using UAV Transmission-Line Imaging
- 引入注意力自编码器融合特征，增强异常信息保留与背景抑制。
- 在绝缘子缺陷数据集上达95.1% mAP，比最强YOLO基线高5.0点。
- 适合电力巡检场景，尤其对罕见缺陷类别敏感，可直接部署于无人机系统。
高电压输电线路绝缘子的自动化缺陷检测因类别严重不平衡、尺度变化大及缺陷空间范围小，在无人机影像中仍具挑战。本文提出AE-YOLO框架，结合注意力引导的自编码器与改进的YOLO结构，用于鲁棒检测。该架构在特征金字塔-路径聚合网络（FPN-PAN）中嵌入轻量级瓶颈自编码器，以保留多尺度特征融合中的异常敏感信息；在主干网络中使用卷积块注意力模块（CBAM），提升特征判别力并抑制背景干扰。同时引入方差最大化自编码器正则化策略，促使潜在表示具有多样性和缺陷区分性。训练采用统一目标函数，融合焦点损失（focal loss）、完整IoU（CIoU）损失与自编码器正则项，缓解前景-背景不平衡并提高定位精度。推理阶段，通过加权框融合（WBF）整合YOLOv8、YOLOv10和YOLO11预测结果，并引入自编码器引导的置信度增强机制，提升对稀有缺陷类别的敏感度。在绝缘子缺陷检测数据集上的实验表明，采用EfficientNetV2主干的AE-YOLO达到95.10% [email protected]、96.40%精度与93.80%召回率，较最强的YOLO家族基线在[email protected]上提升5.0点，召回率提升6.7点。结果验证了该框架的有效性与适应性，为基于无人机的输电线路巡检与缺陷监控提供了实用且可扩展的解决方案。
原文摘要 · Abstract (English)
Automated defect detection in high-voltage transmission-line insulators remains challenging due to severe class imbalance, large scale variation, and the small spatial extent of defect instances in Unmanned Aerial Vehicle (UAV) imagery. To address these challenges, this paper proposes AE-YOLO, an Attention-Guided AutoEncoder-Enhanced YOLO framework for robust insulator defect detection. The architecture integrates lightweight bottleneck autoencoders within a Feature Pyramid Network-Path Aggregation Network (FPN-PAN) neck. This preserves anomaly-sensitive information during multi-scale feature fusion. Convolutional Block Attention Modules (CBAM) are used throughout the backbone, enhancing feature discrimination and suppressing background interference. The framework also introduces a variance-maximizing autoencoder regularization strategy, which encourages diverse, defect-discriminative latent representations. The network trains using a unified objective that combines focal loss, Complete IoU (CIoU) loss, and autoencoder regularization to address foreground-background imbalance and improve localization accuracy. During inference, Weighted Boxes Fusion (WBF) combines predictions from YOLOv8, YOLOv10, and YOLO11. An autoencoder-guided confidence boosting mechanism improves sensitivity to rare defect categories. Experiments on the Insulator-Defect Detection dataset show that AE-YOLO with an EfficientNetV2 backbone achieves 95.10 percent mAP at 0.5, 96.40 percent precision, and 93.80 percent recall. This performance surpasses the strongest YOLO-family baseline by 5.0 points in mAP at 0.5 and 6.7 points in recall. These results confirm the effectiveness and adaptability of the framework. The model is a practical and scalable solution for UAV-based transmission-line inspection and defect monitoring.
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