YOLOX-SAR: High-Precision Object Detection System Based on Visible and Infrared Sensors for SAR Remote Sensing

Abstract

The object detection technology for Synthetic Aperture Radar (SAR) image generation is of great significance in signal processing, radar imaging and other fields. SAR image is the image data obtained from the electromagnetic wave echo from the radar to the object after processing the range and the azimuth respectively. However, the sizes of the objects to be detected change dramatically and the detection difficulty increases, because the scattering characteristics of electromagnetic waves have a great influence on SAR images. At the same time, the large areas of background information will contain confused geographical elements, which seriously affect the detection performance. Therefore, we propose the YOLOX-SAR algorithm to solve these problems. Based on YOLOX, we combine the adaptive activation function Meta-ACON with the SPP module in Backbone to improve the feature extraction ability. We also integrate the Convolutional Block Attention Model (CBAM) at FPN to find the attention area in scenes with dense objects. Furthermore, many useful strategies, such as data enhancement and MS-testing, are provided to improve our proposed YOLOX-SAR. Apart from that, in order to objectively demonstrate the competitive performance of YOLOX-SAR, we compare it with other object detection algorithms on the sensor acquisition dataset of NWPU VHR-10. There remain a large amount of research results demonstrating that YOLOX-SAR possesses good performance as well as interpretation ability in SAR image object detection. The mAP of YOLOX-SAR reaches 89.56%, which is 4.04% higher than that of the previous YOLOX algorithm. YOLOX-SAR, as an improved version of YOLO series, can still be detected in real-time and its FPS reaches 67, keeping the characteristic of fast detection speed of the YOLOX series. Experimental results and analysis show that under the complex electromagnetic scattering background, YOLOX-SAR can ensure the authenticity and effectiveness of the dataset collected by the sensors. Meanwhile, YOLOX-SAR ensures a good balance between the accuracy and running time of object detection. Therefore, the algorithm has good detection performance for objects of various sizes in SAR images.