Improved Faster-RCNN algorithm combined with infrared satellite image for tropical cyclone detection

Abstract

Automatic detection of tropical cyclone (TC) regions from satellite images can provide regions of interest for intelligent TC positioning and intensity determination, and improve the efficiency and accuracy of intelligent disaster weather forecasting. There are currently few studies on automatic detection of TCs from satellite images. In recent years, deep learning technology has developed rapidly in various fields. This paper improves the Faster-RCNN target detection model in deep learning and applies it to the TC detection. The TC detection model designed in this paper is based on the original Faster-RCNN network framework, and the feature extraction network is changed from the original VGG16 network to the ResNet50 network . On this basis, this paper designs a feature fusion network Single Output Feature Fusion Networks (SOFFN). The feature layer used for detection can combine the semantic information of the high-level feature map and the high-resolution feature information of the low-level feature map, fuse different feature layers. At the same time, a new attention mechanism, Channel Linear Weighted Networks (CLWNet), based on the Squeeze-and-Excitation Networks (SENet) channel attention mechanism improvement is added to the model designed in this paper to improve the detection performance. In this paper, China's FY-2D satellite images are used to verify the performance of the proposed model. Experimental results show that the proposed model has achieved good results in TC detection.