摘要： 针对无人机电力巡检中绝缘子缺陷图像存在的目标尺度差异大导致的小目标缺陷漏检率高以及复杂背景下检测精度低等问题，本文提出了时频协同注意力绝缘子缺陷检测算法。首先，为扩大卷积核感受野、增强对图像低频信息的提取能力，在网络主干上集成小波变换卷积模块WTCM；并在此基础上，设计多尺度卷积注意力增强模块MCAAM，通过结合通道与空间注意力机制，进一步抑制复杂背景对绝缘子目标的干扰；其次，为进一步提升模型在复杂环境下的鲁棒性，设计频域调制注意力机制FMAM，这一机制通过融合频域与空域信息，使模型能够更全面地感知图像特征，确保检测结果的稳定性和可靠性；最后，设计自适应加权特征融合AWFF，通过动态调整特征融合权重增强跨维度特征交互，进一步提升网络表征能力。实验结果表明，该算法的mAP50达到92.4%，较基线模型提升4.8%，小目标缺陷召回率提升5.2%，推理速度由112提升到了132。此外，绝缘子损坏、锤子和闪络三类缺陷的AP值分别提高了7.6%、1.7%和9.8%。相比基线模型YOLO11n，改进模型在检测精度与推理效率方面均表现出更优性能。

Abstract: This paper proposes a time–frequency collaborative attention algorithm for insulator defect detection in UAV power inspection. The method is used to address the problems of high missed detection rate of small defects caused by large differences in target scales and low detection accuracy under complex backgrounds.First, a Wavelet Transform Convolution Module (WTCM) is integrated into the backbone network to enlarge the receptive field and enhance the extraction of low-frequency information. Building on this, a Multi-scale Convolutional Attention Augmentation Module (MCAAM) is designed. It combines channel and spatial attention mechanisms to further suppress interference from complex backgrounds. Second, a Frequency-domain Modulation Attention Mechanism (FMAM) is introduced to improve the model’s robustness in complex environments. This mechanism fuses frequency and spatial information, enabling the model to perceive image features more comprehensively and ensuring detection stability. ly, an Adaptive Weighted Feature Fusion (AWFF) module is designed. It dynamically adjusts feature fusion weights to enhance cross-dimensional feature interaction, which further improves the network's representation capability. Experimental results show that the proposed algorithm achieves 92.4% in mAP50, an improvement of 4.8% over the baseline model. The recall rate for small defects increases by 5.2%. The inference speed (FPS) increases from 112 to 132.Furthermore, the AP values for three defect categories—insulator damage, hammer, and flashover—improve by 7.6%, 1.7%, and 9.8%, respectively. Compared to the original YOLO11n model, the improved model demonstrates superior performance in both detection accuracy and inference efficiency.