Image object detection is crucial for the automatic counting of railway tools. However, collected images of railway tools have the characteristics of low illumination, huge difference in the scale of different objects and complex backgrounds. Existing image object detection methods cannot detect railway tools efficiently. In this paper, we propose a novel object detection model which is able to enhance the object-detection ability by fusing multi-scale features. Based on the object detection deep learning model Retinanet, we construct a feature fusion enhancement module. By fusing features, our model can efficiently detect railway tools with different scales. Experiments were conducted based on real-world datasets. Results show that our method is more efficient than Retinanet, and the mAP is increased from 97.85% to 98.11%. By the accurate detection of railway tools in complex backgrounds, our approach can lay the technical foundation for intelligent railway operation and maintenance.