Aiming at the problems of background interference, low image resolution, and large scale variations of foreign objects in the detection of foreign objects on power transmission lines, a foreign object detection model of power transmission line based on improved YOLOv7 is proposed. Firstly, a new backbone network is constructed through space to depth conconvolution(SPD-Conv) and multidimensional collaborative attention(MCA) mechanism to enhance the model's ability to extract features from low-resolution images and to suppress background interference, thus the attention to small foreign objects is increased. Secondly, the output part of the efficient layer aggregation network(ELAN) module is improved by using ghost convolution (Ghost-Conv) to significantly reduce the model's computational complexity. Finally, based on the scalable intersection over union(SIoU) optimized loss function, the model's training speed and robustness are further improved. Experimental results show that the proposed model achieves a mean average precision (mAP) of 95.98% on the power transmission line foreign object detection dataset, which is higher than other mainstream comparative models. At the same time, the frames per second (FPS) reaches 64 to meet the real-time detection requirements of foreign objects of power transmission lines.