The paper introduces RailFOD23, a new dataset designed for foreign object detection on railroad transmission lines. The dataset includes 14,615 high-resolution images with 40,541 annotated objects, covering four common foreign objects: plastic bags, fluttering objects, bird nests, and balloons. These objects can cause power outages and safety issues when they come into contact with power lines. The authors address the challenge of limited and rare anomalies in railroad image data by using a combination of manual synthesis, large-scale models like ChatGPT, and text-to-image generation techniques to synthesize a diverse set of foreign object images. The dataset is publicly available on Figshare, and the paper evaluates the performance of various deep learning models using metrics such as mAP, number of parameters, and confusion matrices. The results show that models like Yolo v8 series perform well in detecting foreign objects, with Yolo v8-s balancing speed and precision effectively. The paper also provides a benchmark for one-stage and two-stage object detection methods, demonstrating the feasibility of deep learning in this context.The paper introduces RailFOD23, a new dataset designed for foreign object detection on railroad transmission lines. The dataset includes 14,615 high-resolution images with 40,541 annotated objects, covering four common foreign objects: plastic bags, fluttering objects, bird nests, and balloons. These objects can cause power outages and safety issues when they come into contact with power lines. The authors address the challenge of limited and rare anomalies in railroad image data by using a combination of manual synthesis, large-scale models like ChatGPT, and text-to-image generation techniques to synthesize a diverse set of foreign object images. The dataset is publicly available on Figshare, and the paper evaluates the performance of various deep learning models using metrics such as mAP, number of parameters, and confusion matrices. The results show that models like Yolo v8 series perform well in detecting foreign objects, with Yolo v8-s balancing speed and precision effectively. The paper also provides a benchmark for one-stage and two-stage object detection methods, demonstrating the feasibility of deep learning in this context.