Detection and Recognition of Spatial Non-Cooperative Objects Based on Improved YOLOX_L

Abstract

In view of the intelligent requirements of spatial non-cooperative target detection and recognition tasks, this paper applies the deep learning method YOLOX_L to the task and draws on YOLOF (You Only Look One-Level Feature) and TOOD (Task-Aligned One-Stage Object Detection), which optimize and improve its detection accuracy to meet the needs of space Task Accuracy Requirements. We improve the FPN (Feature Pyramid Networks) structure and decoupled prediction network in YOLOX_L and perform a validation comparative analysis of the improved YOLOX_L on the VOC2007+2012 and spacecraft dataset. Our experiments conducted on the VOC2007+2012 benchmark show that the proposed method can help YOLOX_L achieve 88.86 mAP, which is higher than YOLOX_L, running at 50 FPS under the image size of 608 × 608. The spatial target detection method based on the improved YOLOX has a detection accuracy rate of 96.28% and a detection speed of 50 FPS on our spacecraft dataset, which prove that the method has certain practical significance and practical value.