Aerial remote sensing images have complex backgrounds and numerous small targets compared to natural images, so detecting targets in aerial images is more difficult. Resource exploration and urban construction planning need to detect targets quickly and accurately in aerial images. High accuracy is undoubtedly the advantage for detection models in target detection. However, high accuracy often means more complex models with larger computational and parametric quantities. Lightweight models are fast to detect, but detection accuracy is much lower than conventional models. It is challenging to balance the accuracy and speed of the model in remote sensing image detection. In this paper, we proposed a new YOLO model. We incorporated the structures of YOLOX-Nano and slim-neck, then used the SPPF module and SIoU function. In addition, we designed a new upsampling paradigm that combined linear interpolation and attention mechanism, which can effectively improve the model's accuracy. Compared with the original YOLOX-Nano, our model had better accuracy and speed balance while maintaining the model's lightweight. The experimental results showed that our model achieved high accuracy and speed on NWPU VHR-10, RSOD, TGRS-HRRSD and DOTA datasets.
Keywords: YOLOX; aerial image; deep learning; lightweight model; object detection.