Aiming at the problems of low detection accuracy and large model size existing in the current foreign object detection algorithms for railway catenaries, this study proposes a foreign object detection algorithm for catenaries (FRDW-YOLOv8) based on the improved YOLOv8. Firstly, we propose the integration of a FasterNet module into the backbone network to construct the C2f-Faster module, which effectively reduces model complexity and enhances computational efficiency. Secondly, the Receptive-Field Coordinate Attention mechanism (RFCA) is introduced in the feature extraction stage to increase the model′s attention to the foreign object areas of the catenary and allocate more attention to them. Then, a dynamic upsampler (Dysample) is adopted in the neck network, which can retain more detailed information of the foreign objects on the catenary. Finally, the WIoU v3 loss function is used to improve the overall performance of the detection model by dynamically adjusting the weight factors. The experimental results show that the mAP value of the improved algorithm reaches 95.1%, which is 2.8% higher than that of the YOLOv8 model, and the floating-point operations and the number of parameters of the model are only 7.3 G and 2.7 M respectively. The improved algorithm further improves the detection accuracy of the model and makes the model lightweight. It fully demonstrates that the detection performance of the improved algorithm is superior to the current mainstream algorithms and can better complete the task of detecting foreign objects on railway catenaries.