Abstract: To address the challenges of small target scale, complex background, and insufficient feature representation in the detection of potential hazards on high-voltage overhead transmission lines, this paper proposes an improved lightweight real-time detection model, LG-DETR. First, a lightweight backbone network, ResNet-WT, is designed by introducing wavelet transform convolution to enhance multi-scale feature extraction while reducing computational complexity. Meanwhile, a frequency-separated self-attention mechanism is adopted in the feature fusion stage to improve the feature interaction module HL-AIFI, thereby mitigating background interference. Then, a cross-level multi-scale information aggregation feature pyramid network CMIAFPN is proposed to optimize feature transmission paths, combined with a gating module to improve feature retention efficiency and prevent detail loss in high-level features. Furthermore, by incorporating the scaling factor of Focal Loss into Wise-IoU, a novel Focal-WIoU loss function is developed to dynamically adjust the weighting of hard and easy samples, thereby enhancing the detection accuracy of small targets. Experimental results demonstrate that LG-DETR achieves a 6.94 percentage point improvement in and 23.9% reduction in parameters on a high-voltage overhead transmission line hazard dataset, verifying the effectiveness of the proposed improvements.

摘要： 针对高压架空输电线路隐患检测中目标尺度小、背景复杂及现有检测模型的特征表征不足等问题，提出一种改进的轻量级实时检测模型LG-DETR。首先，设计轻量化主干网络ResNet-WT，引入小波变换卷积以增强多尺度特征提取能力并降低计算复杂度，同时在特征融合阶段利用分频自注意力机制，改进特征交互模块HL-AIFI，减少背景干扰。然后，提出跨层级多尺度聚合特征融合网络CMIAFPN优化特征传递路径，结合门控模块提升特征信息保留效率，避免高级特征的细节丢失。通过将Focal Loss的缩放因子引入Wise-IoU，提出Focal-WIoU损失函数，动态调整难易样本权重，提升小目标检测精度。实验结果表明，LG-DETR在高压架空输电线路隐患图像数据集上的 较基础模型提升6.94个百分点，并且参数量降低 23.9%，验证了模型改进的有效性。