在输电线路防振锤缺陷检测中，常常面临缺陷样本不平衡、环境背景复杂以及防振锤尺寸较小等挑战。基于此，提出了一种用于输电线路防振锤缺陷检测的FSO-YOLO模型。在YOLOv8s的主干和颈部结构中引入全维动态卷积模块（omni-dimensional dynamic convolution，ODConv），增强了算法对小目标缺陷特征提取和融合的能力；在模型中加入聚焦调制网络（focal modulatoin network，FMNet），提高了模型聚焦关键区域的识别能力；使用样本加权函数Slideloss取代原分类损失函数，以缓解样本不均衡问题。实验结果表明所提出的FSO-YOLO模型能够有效检测输电线路防振锤缺陷，平均精度均值（mean average precision，mAP）可达95.3 %，浮点计算量（floating point operations，GFLOPs）比原模型降低了32 %，检测速度（frames per second，FPS）为51帧/s，可以更加快速精准地定位识别缺陷。

In the defect detection of dampers for transmission lines, practitioners often face challenges such as imbalanced defect datasets, complex environmental backgrounds, and the small size of these dampers. To address these issues, this paper proposes a FSO-YOLO model specifically designed for the defect detection of transmission line dampers. The FSO-YOLO model integrates an omni-dimensional dynamic convolution （ODConv） module into the backbone and neck architectures of YOLOv8s to enhance the algorithm′s capability in extracting and fusing features of small-scale defect targets; a focal modulation network （FMNet） is incorporated，and the model′s ability to focus on and recognize key areas is enhanced; furthermore, a sample weighting function named Slideloss is employed to replace the original classification loss function, thereby mitigating the problem of sample imbalance. Experimental results demonstrate that the proposed FSO-YOLO model is effective in detecting defects in transmission line dampers, achieving an average accuracy （mAP） of 95.3 %, reducing the floating-point operations （GFLOPs） by 32 % compared to the baseline model, and achieving a detection speed of 51 frames per second （FPS）, enabling faster and more accurate localization and identification of defective targets.