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Addressing the key challenges of insulator fault detection in drone-based power inspection scenarios, such as high missed detection rate for small targets, significant interference from complex backgrounds, and insufficient real-time performance, this study proposes an improved YOLOv10n detection model based on multi-scale feature collaborative optimization. By constructing a lightweight adaptive feature extraction network and a hierarchical fusion mechanism of multi-scale semantic enhancement architecture, dynamic deformable grouped convolution and channel recalibration strategies are adopted in the shallow network to enhance the sensitivity to micro-defect features, while a multi-branch dilated convolution pyramid and cross-dimensional attention mechanism are established in the deep network to build cross-scale associations, achieving a collaborative optimization of detection accuracy and computational efficiency. A shape-sensitive InSh-IoU loss function is proposed, which dynamically adjusts the weight coefficient of the bounding box shape to reduce the positioning error of targets with abnormal aspect ratios, enabling more accurate localization of insulators. Verified by a self-built insulator fault dataset, this model maintains real-time detection speed while achieving an average detection accuracy (mAP@0.5) of 97.12%, an improvement of 2.82% over the baseline model.

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针对无人机电力巡检场景下绝缘子故障检测存在的小目标漏检率高、复杂背景干扰显著及实时性不足等关键挑战，本研究提出一种基于多尺度特征协同优化的改进型YOLOv10n检测模型。通过构建轻量化自适应特征提取网络与多尺度语义增强架构的分层融合机制，在浅层网络采用动态可变形分组卷积与通道重校准策略提升微小缺陷特征敏感性，深层网络则通过多分支空洞卷积金字塔与跨维度注意力机制建立跨尺度关联，实现了检测精度与计算效率的协同优化。提出一种形状敏感的InSh-IoU损失函数，通过动态调整边界框形状权重系数，使长宽比异常目标的定位误差降低，能更好定位绝缘子位置。经自建的绝缘子故障数据集验证，本模型在保持实时检测速度的前提下，平均检测精度（mAP@0.5）达到97.12%，较基准模型提升2.82%。

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郑潇栋，硕士研究生，主要研究方向为深度学习、机器视觉与缺陷检测。E-mail: 2053644176@qq.com

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郑潇栋，硕士研究生，主要研究方向为深度学习、机器视觉与缺陷检测。E-mail: 2053644176@qq.com

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