Existing detection methods for insulator defect identification from unmanned aerial vehicles (UAV) struggle with complex background scenes and small objects, leading to suboptimal accuracy and a high number of false positives detection. Using the concept of local attention modeling, this paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. The Efficient Local Attention (ELA) blocks were added into the neck part of the one-stage YOLOv8 architecture to shift the model's attention from background features towards features of insulators with defects. The SCYLLA Intersection-Over-Union (SIoU) criterion function was used to reduce detection loss, accelerate model convergence, and increase the model's sensitivity towards small insulator defects, yielding higher true positive outcomes. Due to a limited dataset, data augmentation techniques were utilized to increase the diversity of the dataset. In addition, we leveraged the transfer learning strategy to improve the model's performance. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second, outperforming the baseline model. This further demonstrates the effectiveness of attention-based convolutional neural networks (CNN) in object detection tasks.

翻译：现有基于无人机（UAV）的绝缘子缺陷检测方法在复杂背景场景和小目标识别方面存在不足，导致检测精度欠佳且误报率较高。本文基于局部注意力建模思想，提出一种新型注意力基础架构YOLO-ELA以解决该问题。我们在单阶段YOLOv8架构的颈部网络嵌入高效局部注意力（ELA）模块，使模型注意力从背景特征转向缺陷绝缘子特征。采用SCYLLA交并比（SIoU）准则函数以降低检测损失、加速模型收敛，并提升模型对小尺寸绝缘子缺陷的敏感度，从而获得更高的真阳性检测结果。针对数据集有限的问题，采用数据增强技术提升数据集多样性。此外，我们通过迁移学习策略进一步提升模型性能。在高分辨率无人机图像上的实验结果表明，本方法以96.9%的mAP0.5指标达到先进水平，实时检测速度达74.63帧/秒，性能优于基线模型。这进一步验证了基于注意力的卷积神经网络（CNN）在目标检测任务中的有效性。