针对现有输电线路异物检测算法中异物尺度大、背景复杂导致的精度低和实时性差的问题，提出了一种基于改进YOLOv8的轻量化模型。首先设计了一种融合双分支架构注意力机制的MobileNetV2的改进特征提取网络，优化模型的参数量，增强特征表达能力。同时，将CBAM注意力机制嵌入到SPPF和PANet模块，增强异物与环境之间的区分度，提高模型在复杂背景下对异物的检测能力，进而提高了检测精度。此外，引入WIoU损失函数，合理分配梯度增益，提高模型的泛化能力和检测框定位精度。实验结果表明，改进YOLOv8的检测精度mAP达到96.12 %，推理速度达到60帧/s，整体性能优于YOLOv8和其他5种主流目标检测模型，且在不同光照的条件下展现出高稳定性和鲁棒性，证明其在复杂环境中的实用性。在嵌入式设备上开展了进一步测试验证，即使在计算资源受限的条件下改进YOLOv8仍能实现准确检测。

In response to the issues of low accuracy and poor real-time performance caused by the large scale of foreign objects and complex backgrounds in existing transmission line foreign object detection algorithms, a lightweight model based on improved YOLOv8 is proposed. Firstly, a modified feature extraction network is designed by integrating a dual-branch architecture with attention mechanisms into MobileNetV2, optimizing the model's parameters and enhancing the feature representation capability. Additionally, the CBAM attention mechanism is embedded into the SPPF and PANet modules to enhance the discrimination between foreign objects and the environment, to improve the model's detection capability in complex backgrounds and consequently enhance the detection accuracy. Furthermore, the WIoU loss function is introduced to properly allocate gradient gains, enhancing the model's generalization ability and detection box localization accuracy. Experimental results demonstrate that the improved YOLOv8 achieves a detection accuracy of 96.12% mAP and an inference speed of 60 frames per second. It outperforms YOLOv8 and five other mainstream object detection models in terms of overall performance. Moreover, it exhibits high stability and robustness under different lighting conditions, proving its practicality in complex environments. Further testing on embedded devices confirms that even under limited computational resources, the improved YOLOv8 can still achieve accurate detection.