SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery

In this paper, we propose an accurate yet fast small object detection method for RSI, named SuperYOLO, which fuses multimodal data and performs high resolution (HR) object detection on multiscale objects by utilizing the assisted super resolution (SR) learning and considering both the detection accuracy and computation cost. First, we construct a compact baseline by removing the Focus module to keep the HR features and significantly overcomes the missing error of small objects. Second, we utilize pixel-level multimodal fusion (MF) to extract information from various data to facilitate more suitable and effective features for small objects in RSI. Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy. Moreover, to avoid introducing additional computation, the SR branch is discarded in the inference stage and the computation of the network model is reduced due to the LR input. Experimental results show that, on the widely used VEDAI RS dataset, SuperYOLO achieves an accuracy of 73.61% (in terms of mAP50), which is more than 10% higher than the SOTA large models such as YOLOv5l, YOLOv5x and RS designed YOLOrs. Meanwhile, the GFOLPs and parameter size of SuperYOLO are about 18.1x and 4.2x less than YOLOv5x. Our proposed model shows a favorable accuracy-speed trade-off compared to the state-of-art models. The code will be open sourced at https://github.com/icey-zhang/SuperYOLO.