A Scalable Target Orientation Detection Method for Remote Sensing Images Based on Improved YOLOX Algorithm

Significant progress has been achieved in the development of oriented target detection algorithms based on deep learning, which have found widespread application in various fields, including remote sensing. However, existing methods struggle with adjusting model size and often exhibit unsatisfactory detection performance for targets that overlap, are large, or have similar backgrounds. To address these challenges, this letter proposes an oriented target detection algorithm called Oriented you only look once X (YOLOX), which integrates several optimization techniques. Specifically, to meet the requirements of oriented detection while enhancing feature extraction, we introduce a new network architecture that includes an orientation detection branch and a multiscale feature fusion module (MSFFM). An MSFFM based on attention weights is proposed to integrate features across scales while minimizing noise. In addition, to mitigate the impact of the number of positive samples on the original loss function and focus the network's attention on learning challenging targets, an object-aware reweighted loss function is introduced in this study. This approach dynamically adjusts the loss contribution for each target. Two models of different sizes are developed using the Oriented YOLOX scaling strategy to cater to scenarios prioritizing either accuracy or speed. Extensive experiments on the dataset for object detection in aerial images (DOTA) and object detection in optical remote sensing images (DIOR-R) datasets demonstrate that Oriented YOLOX performs better in detecting challenging targets. Compared with other oriented target detection methods, this approach not only achieves higher detection accuracy but also reduces parameter counts, improving inference speed.