基于多尺度融合与上下文增强的绝缘子缺陷检测

doi:10.3778/j.issn.1002-8331.2406-0331

摘要/Abstract

摘要： 绝缘子缺陷检测在保障电网的安全、稳定运作中发挥重要作用。受无人机航拍图像中背景区域复杂、目标尺度不一的限制，主流检测算法难以有效检测小尺寸绝缘子缺陷。为解决这一问题，提出一种基于多尺度融合与上下文增强的MFCE-YOLOv8绝缘子缺陷检测算法。设计多尺度信息融合注意力机制（MIAA），利用多尺度信息提高模型对小尺度缺陷的提取能力并减少背景干扰；基于全局与局部信息构建上下文特征学习模块（CFLM），减少深层网络中特征信息的丢失；提出了一种跨层特征融合模块（CFFM），有效促进不同层间信息交互并减少语义冲突。实验表明，MFCE-YOLOv8在绝缘子缺陷数据集上的全类平均精度mAP达到了92.3%，其中闪络和破损缺陷检测精度AP分别为86.0%和91.4%，性能优于其他比较算法。

关键词: 无人机巡检, 绝缘子, 小目标检测, YOLOv8

Abstract: The detection of insulator defects plays a pivotal role in ensuring the steady and reliable operation of power grids. However, due to the complex background and the varying scales, it is very difficult to effectively detect the insulator defect. To address this problem, a YOLOv8 model based on multi-scale fusion and contextual enhancement (MFCE-YOLOv8) is proposed. Firstly, a multi-scale information aggregation attention (MIAA) is developed to increase the capability of the detection of small-scale defects while reducing background interference. Next, to minimize the loss of feature information in the deep network, a context feature learning module (CFLM) is constructed based on global and local information. Finally, a cross-layer feature fusion module (CFFM) is proposed to fully explore information communication between different layers and reduce semantic conflicts. Experimental results show that MFCE-YOLOv8 achieves an overall mean average precision (mAP) of 92.3% on the insulator defect dataset, and obtains the AP of flashover and damage defect detection of 86.0% and 91.4%, respectively, which outperforms some existing methods.

Key words: drone inspection, insulators, small target detection, YOLOv8

曾业战, 陈天航, 邓倩, 彭歆瑶, 欧阳洪波, 钟春良. 基于多尺度融合与上下文增强的绝缘子缺陷检测[J]. 计算机工程与应用, 2025, 61(20): 368-378.

ZENG Yezhan, CHEN Tianhang, DENG Qian, PENG Xinyao, OUYANG Hongbo, ZHONG Chunliang. Insulator Defect Detection Based on Multi-Scale Fusion and Contextual Enhancement[J]. Computer Engineering and Applications, 2025, 61(20): 368-378.

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