Cyclone detection is a classic yet developing topic. Various methods have been developed for the purpose of cyclone detection based on sea level pressure, cloud imagery, wind field, etc. In this study, a data fusion approach that utilizes the data productions from multiple remote sensors is presented. A deep-learning-based object detection algorithm was adopted to form a global-scale cyclone detection model. Wind field data obtained from Mean Wind Field-Advanced Scatterometer (MWF-ASCAT) was integrated with the rainfall intensity data obtained from Global Precipitation Measurement (GPM) as the dataset for model training and testing. Feature pyramid network (FPN), which was designed for small target detection, was integrated with faster-regions with convolutional neural network (faster-RCNN) to detect the cyclones within the fused dataset. The proposed model consists of two modules: a feature extractor and region proposal network (RPN) based on FPN that searches for the potential areas of cyclones within the fused dataset, and a regions of interests (ROI) processor that calibrate the locations of cyclone regions through a fully-connected neural network and a bounding box regression. An ablation experiment was also designed in the study in order to verify the necessity of data fusion. The results from ablation experiment suggested that the wind field data provided more contribution in the cyclone detection than the precipitation data.