基于多尺度特征与通道感知的绝缘子缺陷检测

doi:10.3778/j.issn.1002-8331.2409-0265

摘要/Abstract

摘要： 针对无人机航拍中绝缘子图像背景复杂、缺陷种类多样和尺度差异大等问题，提出一种基于多尺度特征与通道感知的绝缘子缺陷检测算法。通过引入不同层级下的特征图到多尺度特征融合模块，以结合多尺度特征信息，并利用一系列并行深度卷积以充分提取上下文信息；在主干网络中嵌入自适应通道感知下采样模块，将输入特征的空间维度转换为通道维度以实现下采样，确保特征图在下采样过程中避免小尺度特征信息的丢失，并利用高效的通道注意力机制提高模型对重要通道信息的感知能力；在添加小尺度目标检测层的基础上，设计轻量化共享幻影卷积检测头以在减少模型参数量的条件下，提高模型对多尺度目标的检测能力。实验结果表明，所提模型对不同类别及多尺度的绝缘子缺陷均取得了较好的效果，其平均检测精度为91.4%，相较于基准模型提高了5.2个百分点，整体性能优于其他绝缘子缺陷检测算法。

关键词: 缺陷检测, 多尺度特征, 通道感知, 共享卷积

Abstract: In order to solve the problems of complex background, diverse defect types and large-scale differences in insulator images in UAV aerial photography, an insulator defect detection algorithm based on multi-scale features and channel perception is proposed. The feature maps at different levels are introduced into the multi-scale feature fusion module to combine the multi-scale feature information, and a series of parallel deep convolutions are used to fully extract the context information. The adaptive channel-aware down sampling module is embedded in the backbone network, the spatial dimension of the input features is converted into the channel dimension to realize down sampling, so as to ensure that the feature map avoids the loss of small-scale feature information in the down sampling process, and the efficient channel attention mechanism is used to improve the model’s perception ability of important channel information. On the basis of adding a small-scale object detection layer, a lightweight shared ghost convolutional detection head is designed to improve the model’s detection ability of multi-scale targets under the condition of reducing the number of model parameters. Experimental results show that the proposed model has achieved good results for different types and multi-scale insulator defects, and its average detection accuracy is 91.4%, which is 5.2 percentage points higher than that of the benchmark model, and the overall performance is better than that of other insulator defect detection algorithms.

Key words: defect detection, multi-scale features, channel sensing, shared convolution

计凯, 张文斌, 陈小涛. 基于多尺度特征与通道感知的绝缘子缺陷检测[J]. 计算机工程与应用, 2025, 61(24): 240-250.

JI Kai, ZHANG Wenbin, CHEN Xiaotao. Insulator Defect Detection Based on Multi-Scale Features and Channel Perception[J]. Computer Engineering and Applications, 2025, 61(24): 240-250.

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