Remote Sensing Image Objects Detection Algorithm based on Improved YOLOv5

With the development of remote sensing technology, remote sensing images are developing towards higher resolution and larger data volume, and it is important to identify objects from remote sensing images quickly and accurately. Due to the significant differences between remote sensing images and natural images, the direct application of existing algorithms to remote sensing images is not ideal. Therefore, we take the YOLOv5 object detection algorithm as base, and have completed the following tasks: First, we present an adaptive image cutting data preprocessing method, which fills or cuts images into uniform size to cope with the resolution differences of remote sensing images. Second, we use the Mosaic data enhancement method to improve the algorithm's effect in complex backgrounds. Third, we use the Soft-NMS post-processing algorithm to reduce the missed detection of dense objects. Furthermore, we transplant the algorithm to the hardware platform. After the above improvements, the mAP of our algorithm increases from 0.354 to 0.677 on the DOTA dataset, achieving a good object detection effect on remote sensing images; and with the help of the TensorRT, it has reached a detection speed of about 60 FPS on the NVIDIA Jetson AGX Xavier embedded hardware platform.