Abstract: Foreign object attached to the overhead line equipment is one of major hazards affecting the safety of railway train operation. Before operating the trains on a railway line, it is necessary to check whether there are foreign objects attached to the overhead line equipment. At present, the detection of foreign objects attached to the overhead line equipment mainly relies on manual inspection, which has low work efficiency and high consumption of manpower. This article explores the feasibility of using deep learning based object detection technology to realize automatic detection of foreign objects attached to the overhead line equipment through modeling experiments. Three foreign object detection models were constructed: YOLOv5 model, YOLOv5+CA improved model, and YOLOv5+ConvNext Block improved model. Using a data set of overhead line equipment images containing two common foreign objects, i.e. bird nests and lightweight foreign objects, experimental analysis was conducted on the three models. The experimental results show that compared to the YOLOv5 model , the YOLOv5+CA improved model and the YOLOv5+ConvNext Block improved model have better performance in detecting the two common foreign objects. Moreover, the YOLOv5+ConvNext Block improved model has stronger capability to detect small objects.