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.